Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii
There has been an enormous increase in the global demand for energy especially in developing countries as a result of rapid industrial development, population growth and economic growth. Therefore, utilities are continuously planning the expansion of their power generation capacity to meet the incre...
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2012
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my.utm.472572019-03-31T08:37:35Z http://eprints.utm.my/id/eprint/47257/ Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii Hassan, Mohammad Yusri Suharto, M. N. Majid, Md. Shah Abdullah, Md. Pauzi TK Electrical engineering. Electronics Nuclear engineering There has been an enormous increase in the global demand for energy especially in developing countries as a result of rapid industrial development, population growth and economic growth. Therefore, utilities are continuously planning the expansion of their power generation capacity to meet the increasing load demand by augmenting the existing power plant or setting up new power plant at new location. The location of new power plant affects many ways on power system network. This paper presents a multi-objective optimization approach to find the optimal location for installing a new generator in which the economic, environmental and technical aspects are taken into consideration. Hence, a multi-objective approach, based on the Nondominated Sorting Genetic Algorithm-II (NSGA-II), has been employed to minimize simultaneously the cost of generation and emission levels of overall system subject to technical constraints by varying locations of the new generator. Moreover, an approach based on fuzzy set theory is adopted to extract one of the Pareto-optimal solutions as the best compromise solution. The proposed approach is tested on IEEE 30-bus system to illustrate its potential. Results show that the proposed approach is capable of determining the optimal generation location that can save the overall fuel cost as well as reduce the emission levels of generators in the network. The comparison with the classical technique demonstrates the superiority of the proposed algorithm. 2012 Article PeerReviewed Hassan, Mohammad Yusri and Suharto, M. N. and Majid, Md. Shah and Abdullah, Md. Pauzi (2012) Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii. International Review of Electrical Engineering, 6 (5). pp. 2467-2476. ISSN 1827-6660 |
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Universiti Teknologi Malaysia |
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TK Electrical engineering. Electronics Nuclear engineering |
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TK Electrical engineering. Electronics Nuclear engineering Hassan, Mohammad Yusri Suharto, M. N. Majid, Md. Shah Abdullah, Md. Pauzi Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii |
| description |
There has been an enormous increase in the global demand for energy especially in developing countries as a result of rapid industrial development, population growth and economic growth. Therefore, utilities are continuously planning the expansion of their power generation capacity to meet the increasing load demand by augmenting the existing power plant or setting up new power plant at new location. The location of new power plant affects many ways on power system network. This paper presents a multi-objective optimization approach to find the optimal location for installing a new generator in which the economic, environmental and technical aspects are taken into consideration. Hence, a multi-objective approach, based on the Nondominated Sorting Genetic Algorithm-II (NSGA-II), has been employed to minimize simultaneously the cost of generation and emission levels of overall system subject to technical constraints by varying locations of the new generator. Moreover, an approach based on fuzzy set theory is adopted to extract one of the Pareto-optimal solutions as the best compromise solution. The proposed approach is tested on IEEE 30-bus system to illustrate its potential. Results show that the proposed approach is capable of determining the optimal generation location that can save the overall fuel cost as well as reduce the emission levels of generators in the network. The comparison with the classical technique demonstrates the superiority of the proposed algorithm. |
| format |
Article |
| author |
Hassan, Mohammad Yusri Suharto, M. N. Majid, Md. Shah Abdullah, Md. Pauzi |
| author_facet |
Hassan, Mohammad Yusri Suharto, M. N. Majid, Md. Shah Abdullah, Md. Pauzi |
| author_sort |
Hassan, Mohammad Yusri |
| title |
Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii |
| title_short |
Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii |
| title_full |
Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii |
| title_fullStr |
Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii |
| title_full_unstemmed |
Multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii |
| title_sort |
multi-objective optimal generation location using non-dominated sorting genetic algorithm-ii |
| publishDate |
2012 |
| url |
http://eprints.utm.my/id/eprint/47257/ |
| _version_ |
1643652270560116736 |
| score |
13.250246 |