Yazar "Demirbas, Muhammet" seçeneğine göre listele

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    Enhanced Coati Optimization Algorithm for Static and Dynamic Transmission Network Expansion Planning Problems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Demirbas, Muhammet; Dosoglu, M. Kenan; Duman, Serhat
    The power systems are becoming more and more complex due to the inclusion of new components and increasing load demand. Consequently, it is imperative to incorporate additional generation units and transmission links into the system. Transmission Network Expansion Planning (TNEP) seeks to include generation units and transmission lines into the system at optimal locations and minimal costs. Mathematical techniques are extensively employed to address the problem. Nonetheless, mathematical methods necessitate extensive computation durations. Consequently, novel solution strategies are under investigation. The TNEP problem is characterized by an innovative and effective metaheuristic optimization techniques. This study presents a novel Opposition Based Learning and Fitness Distance Balance based Coati Optimization Algorithm (FDBCOA-OBL) designed to address Static and Dynamic TNEP problems. An extensive experimental investigation was undertaken to evaluate the efficacy of the suggested method in addressing the benchmark test suites and the TNEP problem. The FDBCOA-OBL algorithm, utilizing the Elite OBL approach, surpassed all other comparative versions in addressing the benchmark test problems. The Wilcoxon analysis indicates that it lost 6 problems, tied in 110, and won 166 problems. The proposed approach resolved the TNEP problem in 6, 25, and 93-bus test systems. The Static TNEP solution was applied to the 6 and 25 bus test systems, while the Dynamic Multistage TNEP method was utilized in the 93-bus test system. The acquired investment expenses were compared to the research already documented in the literature. The findings indicate that the suggested method demonstrates robust performance.
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    Fuzzy-Based Fitness-Distance Balance Snow Ablation Optimizer Algorithm for Optimal Generation Planning in Power Systems
    (Mdpi, 2025) Demirbas, Muhammet; Duman, Serhat; Ozkaya, Burcin; Balci, Yunus; Ersoy, Deniz; Dosoglu, M. Kenan; Guvenc, Ugur
    Economic dispatch (ED) is one of the most important problems in terms of energy planning, management, and operation in power systems. This study presents a snow ablation optimizer (SAO) algorithm developed with the fuzzy-based fitness-distance balance (FFDB) method for solving ED problems in small-, medium- and large-scale electric power systems and determining the optimal operating values of fossil fuel thermal generation units. The FFDB-based SAO algorithm (FFDBSAO) controls early convergence problems through balancing exploration-exploitation and improves the solving of high-dimensional optimization problems. In the light of extensive experimental studies conducted on CEC2020, CEC2022, and classical benchmark test functions, the FFDBSAO2 algorithm has shown superior performance against its competitors. Wilcoxon and Friedman's statistical analysis results confirm the performance and efficiency of the algorithm. Moreover, the proposed algorithm significantly reduces total fuel cost by optimizing fossil fuel thermal generation units. According to the results, the scalability and robustness of the algorithm make it a valuable tool for solving large-scale optimization problems in the planning of electric power systems.