Ökten, Büşra2017-08-252017-08-252017-082017-082017-08-23http://hdl.handle.net/11693/33550Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2017.Includes bibliographical references (leaves 48-52).With the growing tendency in shifting from centralized to decentralized energy systems, we investigate the sizing decision of a grid-connected decentralized energy system. This system is composed of renewable energy generation components which are solar panels and wind turbines, storage unit and grid connection. In the system, it is aimed to nd the optimal sizes of these components while considering both cost and environment. Resulting from this consideration, there are two objectives which are total cost and carbon dioxide emission in the problem. Together with these two objectives, uncertainty introduced by the renewable energy sources and electricity demand makes the problem stochastic in nature. In order to solve the bi-objective stochastic optimization problem, we establish a metaheuristic-based solution approach in which metaheuristic algorithms and simulation tool are utilized in a simulation-optimization framework. By using three well-known metaheuristic algorithms such as Optimized Multi Objective Particle Swarm Optimization Algorithm (OMOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) in the proposed methodology, a numerical study is carried out for alternative wind, solar and demand scenario sets. We show that OMOPSO is the best performing algorithm to be used in the metahuristic approach. With OMOPSO algorithm's superior performance, metaheuristic approach is compared to a simulation-optimization approach that is previously developed for the same problem by using performance metrics.xiii, 62 leaves : illustrations, charts (some color) ; 29 cm.Englishinfo:eu-repo/semantics/openAccessHybrid Renewable Energy SystemBi-objective Stochastic OptimizationSimulation-based OptimizationMetaheuristic AlgorithmsThesisB156114