Enhancement of radial distribution network based on optimal location and sizing of photoVoltaic distributed generation using mountain gazelle optimizer and eel and grouper optimizer
Due to the growth of loads in distribution networks and the increase in demand for electrical energy, providing energy from traditional sources has become a cause of huge losses, high costs, and environmental pollution. To avoid these problems, clean energy sources are the desired way to solve these...
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| Published in: | International journal on smart sensing and intelligent systems Vol. 18; no. 1 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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| ISSN: | 1178-5608 |
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| Abstract | Due to the growth of loads in distribution networks and the increase in demand for electrical energy, providing energy from traditional sources has become a cause of huge losses, high costs, and environmental pollution. To avoid these problems, clean energy sources are the desired way to solve these problems by integrating Photovoltaic Distribution Generators (PVDG) into Radial Distribution Networks (RDN). Furthermore, detecting the optimal locations and sizes of PVDG is determined by modern approaches of optimization algorithms such as Mountain Gazelle Optimizer (MGO) and Eel and Grouper Optimizer (EGO). The proposed algorithms are applied to Multi-Objective Functions (MOF), including technical objective functions (OFs) and economic OF. Active Power Loss Index (APLI), Reactive Power Loss Index (RPLI), Voltage Deviation Index (VDI), and Voltage Stability Index (VSI) are the technical OFs, while the investment cost of PVDG (ICDG) is the economic OF. The efficiency and effectiveness of the proposed approaches are crucial and are achieved by the implemention of the standard test system (IEEE 69 bus), in addition to the Iraqi RDN (Iraqi 77 bus). Moreover, the simulation results carried out by the proposed algorithms have been verified for accuracy and validity compared with the results of previous articles. The overall performance of the networks is improved after incorporating the optimal allocations of PVDG in RDN; the voltage profile level and VSI are maximized for all buses, while the active and reactive power losses are minimized for all lines. On the other hand, the simulation results showed the dominance of the EGO technique over the MGO technique according to the speed and smoothness of convergence to the best solution with the minimum number of iterations. |
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| AbstractList | Due to the growth of loads in distribution networks and the increase in demand for electrical energy, providing energy from traditional sources has become a cause of huge losses, high costs, and environmental pollution. To avoid these problems, clean energy sources are the desired way to solve these problems by integrating Photovoltaic Distribution Generators (PVDG) into Radial Distribution Networks (RDN). Furthermore, detecting the optimal locations and sizes of PVDG is determined by modern approaches of optimization algorithms such as Mountain Gazelle Optimizer (MGO) and Eel and Grouper Optimizer (EGO). The proposed algorithms are applied to Multi-Objective Functions (MOF), including technical objective functions (OFs) and economic OF. Active Power Loss Index (APLI), Reactive Power Loss Index (RPLI), Voltage Deviation Index (VDI), and Voltage Stability Index (VSI) are the technical OFs, while the investment cost of PVDG (ICDG) is the economic OF. The efficiency and effectiveness of the proposed approaches are crucial and are achieved by the implemention of the standard test system (IEEE 69 bus), in addition to the Iraqi RDN (Iraqi 77 bus). Moreover, the simulation results carried out by the proposed algorithms have been verified for accuracy and validity compared with the results of previous articles. The overall performance of the networks is improved after incorporating the optimal allocations of PVDG in RDN; the voltage profile level and VSI are maximized for all buses, while the active and reactive power losses are minimized for all lines. On the other hand, the simulation results showed the dominance of the EGO technique over the MGO technique according to the speed and smoothness of convergence to the best solution with the minimum number of iterations. |
| Author | Salman, Ghassan Abdullah Al-Bahrani, Layth Tawfeeq |
| Author_xml | – sequence: 1 givenname: Ghassan Abdullah surname: Salman fullname: Salman, Ghassan Abdullah email: ghassanabdullah@uomustansiriyah.edu.iq organization: Department of Electrical Power and Machines, College of Engineering, University of Diyala, Diyala, Iraq – sequence: 2 givenname: Layth Tawfeeq surname: Al-Bahrani fullname: Al-Bahrani, Layth Tawfeeq organization: Department of Electrical Engineering, College of Engineering, Mustansiriyah University, Baghdad, Iraq |
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| DOI | 10.2478/ijssis-2025-0057 |
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International Journal of Electrical Power & Energy Systems doi: 10.1016/j.ijepes.2014.06.031 |
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| SubjectTerms | Iraqi radial networks multi-objective functions optimization algorithms (MGO and EGO) photovoltaic distribution generators radial distribution networks |
| Title | Enhancement of radial distribution network based on optimal location and sizing of photoVoltaic distributed generation using mountain gazelle optimizer and eel and grouper optimizer |
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