A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm
A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/643791 |
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| author | Wanxing Sheng Ke-yan Liu Yongmei Liu Xiaoli Meng Xiaohui Song |
| author_facet | Wanxing Sheng Ke-yan Liu Yongmei Liu Xiaoli Meng Xiaohui Song |
| author_sort | Wanxing Sheng |
| collection | DOAJ |
| description | A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2), a particle swarm optimization (PSO) algorithm, and nondominated sorting genetic algorithm II (NGSA-II). The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system. |
| format | Article |
| id | doaj-art-90b025ad5ee14de6876552a72512b08d |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-90b025ad5ee14de6876552a72512b08d2025-02-03T05:57:52ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/643791643791A New DG Multiobjective Optimization Method Based on an Improved Evolutionary AlgorithmWanxing Sheng0Ke-yan Liu1Yongmei Liu2Xiaoli Meng3Xiaohui Song4Power Distribution Research Department, China Electric Power Research Institute, No. 15, Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, ChinaPower Distribution Research Department, China Electric Power Research Institute, No. 15, Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, ChinaPower Distribution Research Department, China Electric Power Research Institute, No. 15, Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, ChinaPower Distribution Research Department, China Electric Power Research Institute, No. 15, Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, ChinaPower Distribution Research Department, China Electric Power Research Institute, No. 15, Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, ChinaA distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2), a particle swarm optimization (PSO) algorithm, and nondominated sorting genetic algorithm II (NGSA-II). The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.http://dx.doi.org/10.1155/2013/643791 |
| spellingShingle | Wanxing Sheng Ke-yan Liu Yongmei Liu Xiaoli Meng Xiaohui Song A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm Journal of Applied Mathematics |
| title | A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm |
| title_full | A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm |
| title_fullStr | A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm |
| title_full_unstemmed | A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm |
| title_short | A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm |
| title_sort | new dg multiobjective optimization method based on an improved evolutionary algorithm |
| url | http://dx.doi.org/10.1155/2013/643791 |
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