A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models |
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| Affiliation: | 1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;2. Kyungpook National University, Daegu, Republic of Korea;3. Department of Engineering, University of Cambridge, UK;1. Power Distribution Research Department, China Electric Power Research Institute, Beijing 100192, China;2. China North Vehicle Research Institute, Beijing 100072, China;1. Young Researchers and Elites Club, Borujerd Branch, Islamic Azad University, Borujerd, Iran;2. Faculty of Engineering, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran |
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| Abstract: | In this paper a new and efficient hybrid multi-objective optimization algorithm is proposed for optimal placement and sizing of the Distributed generations (DGs) in radial distribution systems. A Multi-objective Shuffled Bat algorithm is proposed to evaluate the impact of DG placement and sizing for an optimal improvement of the distribution system with different load models. In this study, the ideal sizes and locations of DG units are found by considering the power losses, cost and voltage deviation as objective functions to minimize. Furthermore, the study is verified with voltage dependent load models like industrial, residential, commercial and mixed load models. The feasibility of the proposed technique is verified with the 33 bus distribution network and also the qualitative comparisons against a well-known technique, known as Non-dominated Sorting Genetic Algorithm II (NSGA-II) is done and results are presented. |
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| Keywords: | Distributed generation Power loss minimization Multi-objective optimization algorithm Shuffled Bat algorithm Load models |
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