Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study
With the rapid development of wind turbine, photovoltaicand battery technologies, renewable energy resources such as wind and solar become the most common distributedgenerations (DG) that are being integrated into microgrids. One key impediment is to determine the sizes and placements of DGs within...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Croatian Communications and Information Society (CCIS)
2014-06-01
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| Series: | Journal of Communications Software and Systems |
| Subjects: | |
| Online Access: | https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/134 |
| _version_ | 1819039801270075392 |
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| author | Rui Huang Yubo Wang Chi-Cheng Chu Rajit Gadh Yu-jin Song |
| author_facet | Rui Huang Yubo Wang Chi-Cheng Chu Rajit Gadh Yu-jin Song |
| author_sort | Rui Huang |
| collection | DOAJ |
| description | With the rapid development of wind turbine, photovoltaicand battery technologies, renewable energy resources such as wind and solar become the most common distributedgenerations (DG) that are being integrated into microgrids. One key impediment is to determine the sizes and placements of DGs within which the microgrid can achieve its maximum potential benefits. The objective of the paper is to study and propose an approach to find the optimal sizes and placements of DGs in a microgrid. The authors propose a comprehensive objective function with practical constraints which take all the important factors that will impact the reliability of the power grid into account. To solve the optimization problem, genetic algorithm (GA) is used and compared with a mathematical optimization method nonlinear programming. The proposed model is tested on a real microgrid, i.e. Jeju Island, to evaluate and validate the performances of the approach. The simulation results present the optimal configuration of DGs for Jeju Island power grid. The analysis on results shows that GA maintains a delicate balance between performance and complexity. It is concluded that GA performs better not only in accuracy, stability, but also in computation time. |
| first_indexed | 2024-12-21T08:58:58Z |
| format | Article |
| id | doaj.art-ebafbabbc02d44e1a229914dbb496e83 |
| institution | Directory Open Access Journal |
| issn | 1845-6421 1846-6079 |
| language | English |
| last_indexed | 2024-12-21T08:58:58Z |
| publishDate | 2014-06-01 |
| publisher | Croatian Communications and Information Society (CCIS) |
| record_format | Article |
| series | Journal of Communications Software and Systems |
| spelling | doaj.art-ebafbabbc02d44e1a229914dbb496e832022-12-21T19:09:31ZengCroatian Communications and Information Society (CCIS)Journal of Communications Software and Systems1845-64211846-60792014-06-01102135144Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case StudyRui HuangYubo WangChi-Cheng ChuRajit GadhYu-jin SongWith the rapid development of wind turbine, photovoltaicand battery technologies, renewable energy resources such as wind and solar become the most common distributedgenerations (DG) that are being integrated into microgrids. One key impediment is to determine the sizes and placements of DGs within which the microgrid can achieve its maximum potential benefits. The objective of the paper is to study and propose an approach to find the optimal sizes and placements of DGs in a microgrid. The authors propose a comprehensive objective function with practical constraints which take all the important factors that will impact the reliability of the power grid into account. To solve the optimization problem, genetic algorithm (GA) is used and compared with a mathematical optimization method nonlinear programming. The proposed model is tested on a real microgrid, i.e. Jeju Island, to evaluate and validate the performances of the approach. The simulation results present the optimal configuration of DGs for Jeju Island power grid. The analysis on results shows that GA maintains a delicate balance between performance and complexity. It is concluded that GA performs better not only in accuracy, stability, but also in computation time.https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/134distributed generationoptimizationgenetic algorithm |
| spellingShingle | Rui Huang Yubo Wang Chi-Cheng Chu Rajit Gadh Yu-jin Song Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study Journal of Communications Software and Systems distributed generation optimization genetic algorithm |
| title | Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study |
| title_full | Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study |
| title_fullStr | Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study |
| title_full_unstemmed | Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study |
| title_short | Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study |
| title_sort | optimal configuration of distributed generation on jeju island power grid using genetic algorithm a case study |
| topic | distributed generation optimization genetic algorithm |
| url | https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/134 |
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