The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

The global demand for energy has increased exponentially over the years. To reduce the dominance of fossil fuels in energy production, there has been a shift towards energy production models based on renewable sources. In the design of hybrid energy systems, it is essential to keep investment costs low while ensuring the security of the energy supply by meeting the consumer’s energy demands without interruption. The success of a good energy production model can be directly associated with the results of load estimation. The primary objective of this research is to predict the electricity demand for the Göksun district until 2028, utilising a data set that encompasses electricity usage from 2019 through the first four months of 2024 for the Göksun district in Kahramanmaraş. This endeavour includes the application of various machine learning (ML) paradigms (long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN)-LSTM, support vector regression (SVR)) to produce load forecasting outcomes and to engineer an optimally performing hybrid system. On evaluation of the performance metrics derived from the experimental data, it has been established that the LSTM model outperforms other methodologies, yielding more favourable results. The simulation studies of the designed hybrid system were conducted using the hybrid optimisation model for electric renewables software (HOMER Pro), demonstrating improvements in both economic and environmental parameters. Our study is unique in that it is the first to utilise a data set specific to the Göksun region and to model predictions obtained from this data set using HOMER software.

Bu çalışmada, optimal yük (güç) akışı (OYA) probleminin doğrusal olmayan ve dışbükey olmayan yapısını etkin biçimde çözmek için ilk kez Hipopotam Optimizasyon (HO) algoritması OYA’ya uyarlanmış ve çoklu değişkenlere özgü sınırlar için modifiye edilmiştir. Orijinal HO’nun tekil sınır yaklaşımı, geliştirilen sınır_kontrolü fonksiyonu ile her kontrol değişkenine özgü alt–üst limitleri gözetebilecek şekilde genişletilmiştir. Keşif, savunma ve sömürü fazlarının dengeli etkileşimi hızlı yakınsama ve yerel minimumlardan kaçış sağlamaktadır. Önerilen yöntem yakıt maliyeti ve aktif güç kaybını eşitlik ve eşitsizlik kısıtları altında optimize etmektedir. IEEE 14 baralı sistemde başlangıç yakıt maliyeti 842,34 $/h’den 828,1560 $/h’ye düşürülmüş; SKH, PSOGSA, MSG-HS, MPSO ve PSO-ANN algoritmalarına karşı 1,184–6,204 $/h aralığında ek iyileşme elde edilmiştir. Türkiye 22 baralı sisteminde de Gradient ve ABC tabanlı yöntemlere kıyasla hem maliyet hem aktif kayıp hedeflerinde üstün sonuçlar elde edilmiştir. HO'nun diğer modern algoritmalara kıyasla daha hızlı yakınsadığını ve daha kararlı sonuçlar ürettiğini ortaya koymaktadır. Bulgular, modifiye HO’nun OYA için etkin, sağlam ve ölçeklenebilir bir alternatif sunduğunu göstermektedir.