Photovoltaic Generation Integration with Radial Feeders Using GA and GIS
The electric power generated from different electricity sources are not used efficiently by end users in the world. This is due to the loss of power supplied in the case of electricity transmission and distribution to residential, commercial, and industrial loads. Even if the loss of power in the po...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2020/8854711 |
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| author | Elias Mandefro Getie Belachew Bantyirga Gessesse Tewodros Gera Workneh |
| author_facet | Elias Mandefro Getie Belachew Bantyirga Gessesse Tewodros Gera Workneh |
| author_sort | Elias Mandefro Getie |
| collection | DOAJ |
| description | The electric power generated from different electricity sources are not used efficiently by end users in the world. This is due to the loss of power supplied in the case of electricity transmission and distribution to residential, commercial, and industrial loads. Even if the loss of power in the power system cannot be avoided 100%, it should be reduced to the minimum optimal value. The loss of power in the radial feeders can be minimized using an optimally allocated photovoltaic (PV) generation system by considering the information of geography, solar irradiance of the site, and space availability, which should not have shadow from large buildings and trees. The PV generation system eliminates the problem of power demand by enhancing the capacity of the power network as well as by reducing the depletion and consumption of fossil fuel resources. To reduce power loss and improve system loading capacity for demand response, the integration and finding the optimal place of photovoltaic generation take high concern from power system operators and technicians. The optimal allocation of PV has been done using the Genetic Algorithm (GA) for optimization of a multiobjective function with different constraints. The main objective of this paper is to minimize the power loss of the radial distribution networks by maintaining the phase voltage of the load in balance and improving the drop in voltage along the phase. So, GA is used to determine the best location and capacity of PV generation that can reduce the loss of power in the system. The IEEE-33 bus system is used to test the proposed method. Generally, using the GA and GIS methods results in a high accuracy for optimal placement of PV generation in the IEEE-33 bus radial feeder and enables to reduce the loss of power during transmission and distribution by maintaining the power quality for consumers. |
| format | Article |
| id | doaj-art-93ba6e175e7b492baaf7511f5f8a675e |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-93ba6e175e7b492baaf7511f5f8a675e2025-02-03T05:51:15ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2020-01-01202010.1155/2020/88547118854711Photovoltaic Generation Integration with Radial Feeders Using GA and GISElias Mandefro Getie0Belachew Bantyirga Gessesse1Tewodros Gera Workneh2Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, EthiopiaBahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, EthiopiaBahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, EthiopiaThe electric power generated from different electricity sources are not used efficiently by end users in the world. This is due to the loss of power supplied in the case of electricity transmission and distribution to residential, commercial, and industrial loads. Even if the loss of power in the power system cannot be avoided 100%, it should be reduced to the minimum optimal value. The loss of power in the radial feeders can be minimized using an optimally allocated photovoltaic (PV) generation system by considering the information of geography, solar irradiance of the site, and space availability, which should not have shadow from large buildings and trees. The PV generation system eliminates the problem of power demand by enhancing the capacity of the power network as well as by reducing the depletion and consumption of fossil fuel resources. To reduce power loss and improve system loading capacity for demand response, the integration and finding the optimal place of photovoltaic generation take high concern from power system operators and technicians. The optimal allocation of PV has been done using the Genetic Algorithm (GA) for optimization of a multiobjective function with different constraints. The main objective of this paper is to minimize the power loss of the radial distribution networks by maintaining the phase voltage of the load in balance and improving the drop in voltage along the phase. So, GA is used to determine the best location and capacity of PV generation that can reduce the loss of power in the system. The IEEE-33 bus system is used to test the proposed method. Generally, using the GA and GIS methods results in a high accuracy for optimal placement of PV generation in the IEEE-33 bus radial feeder and enables to reduce the loss of power during transmission and distribution by maintaining the power quality for consumers.http://dx.doi.org/10.1155/2020/8854711 |
| spellingShingle | Elias Mandefro Getie Belachew Bantyirga Gessesse Tewodros Gera Workneh Photovoltaic Generation Integration with Radial Feeders Using GA and GIS International Journal of Photoenergy |
| title | Photovoltaic Generation Integration with Radial Feeders Using GA and GIS |
| title_full | Photovoltaic Generation Integration with Radial Feeders Using GA and GIS |
| title_fullStr | Photovoltaic Generation Integration with Radial Feeders Using GA and GIS |
| title_full_unstemmed | Photovoltaic Generation Integration with Radial Feeders Using GA and GIS |
| title_short | Photovoltaic Generation Integration with Radial Feeders Using GA and GIS |
| title_sort | photovoltaic generation integration with radial feeders using ga and gis |
| url | http://dx.doi.org/10.1155/2020/8854711 |
| work_keys_str_mv | AT eliasmandefrogetie photovoltaicgenerationintegrationwithradialfeedersusinggaandgis AT belachewbantyirgagessesse photovoltaicgenerationintegrationwithradialfeedersusinggaandgis AT tewodrosgeraworkneh photovoltaicgenerationintegrationwithradialfeedersusinggaandgis |