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Multi-objective optimization of the distribution network reconfiguration and distributed generation resources using the improved model of the teaching and learning based method algorithm
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| Aliakbar Akbari *, Hossein Karimianfard |
| , aliakbarakbari6666@gmail.com |
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Abstract: (52 Views) |
| Today, issues such as restructuring, environmental issues, problems and limitations in transmission and distribution lines have led to the increasing use of distributed generation (DG) systems. DG units, according to the characteristics, technology and location of connection to the network, can cause positive effects such as improving the voltage profile, reducing power losses and reliability in distribution networks; Therefore, with the increase in the use of scattered production resources as well as the technical and financial issues of these technologies, new issues such as determining the capacity and location of connecting these equipments to the network have been investigated. The problem of rearranging the distribution network is another optimization problem in distribution networks, so that it optimizes the network for the intended purpose without the need for new equipment. In this article, using an improved model of the learning and teaching-based optimization algorithm (TLBO), simultaneous simulation of distributed generation location problem and distribution network reconfiguration problem, in order to optimize the voltage profile, increase the voltage stability index and reducing network power losses. The proposed method has been implemented with different scenarios on radial networks of 33 buses and 69 buses, and the results obtained after comparing with other methods show the efficiency of the proposed method. |
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| Keywords: teaching-learning-based optimization algorithm, reconfiguration, voltage profile, power loss, distributed generation, voltage stability index. |
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Full-Text [PDF 1118 kb]
(22 Downloads)
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Type of Study: Research |
Subject:
Energy Management, Conservation and Rational Use of Energy
Received: 2022/07/31 | Accepted: 2022/12/19 | Published: 2022/09/22
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