Yazar "Elbaz, A." seçeneğine göre listele

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    Multi-objective Optimization of Combined Economic Emission Dispatch Problem in Solar PV Energy Using a Hybrid Bat-Crow Search Algorithm
    (Gazi Universitesi, 2021) Elbaz, A.; Güneser, M.T.
    This paper deals with the multi-objective fuel cost optimization of a conventional power plant (CPP) and emission minimization in CPPs and solar PV power plants (SPVPPs) using a hybrid bat-crow search algorithm. To resolve this complicated, non-convex, and excessively nonlinear problem, a variety of meta-heuristic optimization algorithms are developed and effectively employed. To handle evolutionary multi-objective algorithms’ inadequacies, such as early convergences, slowly meeting the Pareto-optimal front, and narrow trapping, applying a combination of different algorithms is unusual. This paper offers a hybrid evolutionary multi-objective optimization process based on combining the crow search optimization with the bat algorithm for dealing with the combined economic emission dispatch problem for SPVPPs. A hybrid technique combined with the proposed constriction handling method can balance exploitation and exploration tasks. Different IEEE standard bus systems were tested with the proposed hybrid method using the quadratic cost function and monitoring the transmission losses. The results of the proposed algorithm have also been compared with those of the bat, PSO, and crow search algorithms. The proposed method can be said to be effective considering the simulation results. © 2021, International Journal of Renewable Energy Research. All Rigths Reserved.
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    Optimal sizing of a renewable energy hybrid system in Libya using integrated crow and particle swarm algorithms
    (ASTES Publishers, 2021) Elbaz, A.; Güneser, M.T.
    Sizing optimization should be used to design an efficient, sustainable, and feasible hybrid system. In this paper, a hybrid power plant consisting of an off-grid photovoltaic and wind energy system was planned to supply the demand of residential houses in Libya. To minimize installation and operational costs by sizing each part of the hybrid system, the crow search technique was applied. We optimized the number of photovoltaic modules, wind turbine power, and battery capacity and then we compared the performance of the crow algorithm with the particle swarm optimization algorithm for hybrid system design. The results of the crow algorithm suggest better efficiency for sizing lower-cost hybrid power plants consisting of photovoltaic and wind systems. © 2021 ASTES Publishers. All rights reserved.
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    Using Crow Algorithm for Optimizing Size of Wind Power Plant/Hybrid PV in Libya
    (Institute of Electrical and Electronics Engineers Inc., 2019) Elbaz, A.; Guneser, M.T.
    To design an efficient, sustainable and feasible hybrid system sizing optimization should be applied. In this study, a hybrid power plant, which consists of an off-grid PV and wind energy system to supply the demand of solar energy research center in Libya, was designed. Crow search technique was applied to decrease the installation cost and operating cost by sizing each part of hybrid system. We optimized the number of PV modules, powers of wind turbines and capacities batteries. After offering the design of hybrid system, we compared performance of crow algorithm with particle swarm optimization algorithm performance. Regarding the comparison, crow algorithm results sign a better performance for sizing a lower cost hybrid power plant consists of PV and wind systems. © 2019 IEEE.