Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm
Hybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of the microgrid (MG), HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8822765 |
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| _version_ | 1832559745098055680 |
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| author | Wenhua Li Guo Zhang Xu Yang Zhang Tao Hu Xu |
| author_facet | Wenhua Li Guo Zhang Xu Yang Zhang Tao Hu Xu |
| author_sort | Wenhua Li |
| collection | DOAJ |
| description | Hybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of the microgrid (MG), HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems. |
| format | Article |
| id | doaj-art-19d5cc2dbd7049fda51a5b27dd5555a6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-19d5cc2dbd7049fda51a5b27dd5555a62025-02-03T01:29:18ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/88227658822765Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization AlgorithmWenhua Li0Guo Zhang1Xu Yang2Zhang Tao3Hu Xu4College of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaState Key Laboratory of Astronautic Dynamics, Xi’an 710043, ChinaHybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of the microgrid (MG), HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems.http://dx.doi.org/10.1155/2021/8822765 |
| spellingShingle | Wenhua Li Guo Zhang Xu Yang Zhang Tao Hu Xu Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm Complexity |
| title | Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm |
| title_full | Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm |
| title_fullStr | Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm |
| title_full_unstemmed | Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm |
| title_short | Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm |
| title_sort | sizing a hybrid renewable energy system by a coevolutionary multiobjective optimization algorithm |
| url | http://dx.doi.org/10.1155/2021/8822765 |
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