Optimal Reconfiguration of Intelligent Distribution Net-works in the Presence of DGs using TLBO Algorithm
(DG) is a better alternative to meet power demand near the load centers than centralized power generation. Optimal placement and sizing of DGs plays a crucial role in improving the performance of distribution systems in terms of network loss reduction, voltage profile improvement, reliability of power supply and stability issues. In this paper, a new method is proposed to solve intelligent distribution network reconfiguration in the presence of DGs aiming to minimize active power loss and improve voltage profile. Meta-heuristic optimization algorithm based on training-learning (TLBO) is used to reconfigure distribution network. In order to investigate different operational conditions of distribution systems and efficiency of the proposed method, different scenarios of network reconfiguration are simulated and studied. In order to illustrate performance and effectiveness of the proposed method, IEEE-33 bus radial distribution network is simulated at three different levels. In the proposed scenarios, presence or absence of DGs in different buses and effect of capacity of these sources is studied and simulated.
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