Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization
At present, renewable energy sources (RESs) integration using microgrid (MG) technology is of great importance for demand side management. Optimization of MG provides enhanced generation from RES at minimum operation cost. The microgrid optimization problem involves a large number of variables and c...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/2158926 |
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| author | Pawan Singh Baseem Khan |
| author_facet | Pawan Singh Baseem Khan |
| author_sort | Pawan Singh |
| collection | DOAJ |
| description | At present, renewable energy sources (RESs) integration using microgrid (MG) technology is of great importance for demand side management. Optimization of MG provides enhanced generation from RES at minimum operation cost. The microgrid optimization problem involves a large number of variables and constraints; therefore, it is complex in nature and various existing algorithms are unable to handle them efficiently. This paper proposed an artificial shark optimization (ASO) method to remove the limitation of existing algorithms for solving the economical operation problem of MG. The ASO algorithm is motivated by the sound sensing capability of sharks, which they use for hunting. Further, the intermittent nature of renewable energy sources is managed by utilizing battery energy storage (BES). BES has several benefits. However, all these benefits are limited to a certain fixed area due to the stationary nature of the BES system. The latest technologies, such as electric vehicle technologies (EVTs), provide all benefits of BES along with mobility to support the variable system demands. Therefore, in this work, EVTs incorporated grid connected smart microgrid (SMG) system is introduced. Additionally, a comparative study is provided, which shows that the ASO performs relatively better than the existing techniques. |
| format | Article |
| id | doaj-art-7dd4dd1d10d9427b934e9ed7d9dc8ffd |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7dd4dd1d10d9427b934e9ed7d9dc8ffd2025-02-03T06:11:08ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/21589262158926Smart Microgrid Energy Management Using a Novel Artificial Shark OptimizationPawan Singh0Baseem Khan1School of Informatics, Hawassa University Institute of Technology, Hawassa, EthiopiaSchool of Electrical & Computer Engineering, Hawassa University Institute of Technology, Hawassa, EthiopiaAt present, renewable energy sources (RESs) integration using microgrid (MG) technology is of great importance for demand side management. Optimization of MG provides enhanced generation from RES at minimum operation cost. The microgrid optimization problem involves a large number of variables and constraints; therefore, it is complex in nature and various existing algorithms are unable to handle them efficiently. This paper proposed an artificial shark optimization (ASO) method to remove the limitation of existing algorithms for solving the economical operation problem of MG. The ASO algorithm is motivated by the sound sensing capability of sharks, which they use for hunting. Further, the intermittent nature of renewable energy sources is managed by utilizing battery energy storage (BES). BES has several benefits. However, all these benefits are limited to a certain fixed area due to the stationary nature of the BES system. The latest technologies, such as electric vehicle technologies (EVTs), provide all benefits of BES along with mobility to support the variable system demands. Therefore, in this work, EVTs incorporated grid connected smart microgrid (SMG) system is introduced. Additionally, a comparative study is provided, which shows that the ASO performs relatively better than the existing techniques.http://dx.doi.org/10.1155/2017/2158926 |
| spellingShingle | Pawan Singh Baseem Khan Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization Complexity |
| title | Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization |
| title_full | Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization |
| title_fullStr | Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization |
| title_full_unstemmed | Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization |
| title_short | Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization |
| title_sort | smart microgrid energy management using a novel artificial shark optimization |
| url | http://dx.doi.org/10.1155/2017/2158926 |
| work_keys_str_mv | AT pawansingh smartmicrogridenergymanagementusinganovelartificialsharkoptimization AT baseemkhan smartmicrogridenergymanagementusinganovelartificialsharkoptimization |