Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms
In this paper, the approach focused on the variables involved in assessing the quality of a distributed generation system are reviewed in detail, for its investigation and research contribution. The aim to minimize the electric power losses (unused power consumption) and optimize the voltage profile...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/10/7/960 |
| _version_ | 1811308260499652608 |
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| author | Ahmed Ali Sanjeevikumar Padmanaban Bhekisipho Twala Tshilidzi Marwala |
| author_facet | Ahmed Ali Sanjeevikumar Padmanaban Bhekisipho Twala Tshilidzi Marwala |
| author_sort | Ahmed Ali |
| collection | DOAJ |
| description | In this paper, the approach focused on the variables involved in assessing the quality of a distributed generation system are reviewed in detail, for its investigation and research contribution. The aim to minimize the electric power losses (unused power consumption) and optimize the voltage profile for the power system under investigation. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple Distribution Generation DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA), and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of a longer processing time. |
| first_indexed | 2024-04-13T09:19:51Z |
| format | Article |
| id | doaj.art-66ebb79454514dc08f571203fde351ea |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T09:19:51Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-66ebb79454514dc08f571203fde351ea2022-12-22T02:52:38ZengMDPI AGEnergies1996-10732017-07-0110796010.3390/en10070960en10070960Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization AlgorithmsAhmed Ali0Sanjeevikumar Padmanaban1Bhekisipho Twala2Tshilidzi Marwala3Faculty of Engineering and Built Environment, Department of Electrical and Electronics Engineering Science, University of Johannesburg, Auckland Park 2006, South AfricaFaculty of Engineering and Built Environment, Department of Electrical and Electronics Engineering Science, University of Johannesburg, Auckland Park 2006, South AfricaFaculty of Engineering and Built Environment, Department of Electrical and Electronics Engineering Science, University of Johannesburg, Auckland Park 2006, South AfricaFaculty of Engineering and Built Environment, Department of Electrical and Electronics Engineering Science, University of Johannesburg, Auckland Park 2006, South AfricaIn this paper, the approach focused on the variables involved in assessing the quality of a distributed generation system are reviewed in detail, for its investigation and research contribution. The aim to minimize the electric power losses (unused power consumption) and optimize the voltage profile for the power system under investigation. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple Distribution Generation DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA), and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of a longer processing time.https://www.mdpi.com/1996-1073/10/7/960optimizationsimulated annealinggenetic algorithmpower lossespower consumption |
| spellingShingle | Ahmed Ali Sanjeevikumar Padmanaban Bhekisipho Twala Tshilidzi Marwala Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms Energies optimization simulated annealing genetic algorithm power losses power consumption |
| title | Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms |
| title_full | Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms |
| title_fullStr | Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms |
| title_full_unstemmed | Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms |
| title_short | Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms |
| title_sort | electric power grids distribution generation system for optimal location and sizing a case study investigation by various optimization algorithms |
| topic | optimization simulated annealing genetic algorithm power losses power consumption |
| url | https://www.mdpi.com/1996-1073/10/7/960 |
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