Energy management and optimized operation of renewable sources and electric vehicles based on microgrid using hybrid gravitational search and pattern search algorithm
•Considering renewable energy resources.•Reporting superior solutions in the case of cost and execution time•Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management•Presenting Hybrid Gravitational Search and Pattern Search Algorithm The use of plug-in hybrid electric v...
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| Published in: | Sustainable cities and society Vol. 75; p. 103279 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2021
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| ISSN: | 2210-6707, 2210-6715 |
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| Abstract | •Considering renewable energy resources.•Reporting superior solutions in the case of cost and execution time•Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management•Presenting Hybrid Gravitational Search and Pattern Search Algorithm
The use of plug-in hybrid electric vehicles (PHEVs) can solve many environmental problems and energy crises around the world. Using a large number of PHEVs with high storage and control capabilities can enhance the flexibility of distribution networks. However, optimal management PHEVs with the presence of renewable energy sources (RESs) is one of the main challenges that must be addressed. The optimal management of various RESs along with PHEVs is possible in the form of a microgrid (MG). Moreover, the uncertainties of input parameters are successfully considered in the model development by Monte Carlo simulation (MCS). In the modelling, the uncertainties in PHEVs, load, RESs and energy price are considered and simulated for 24 h. The NiMH-Battery is also used to investigate the role of the storage device. The objective function is minimizing the total cost of the grid-connected MG including the costs of load supply, PHEVs charging demand and power losses. The optimization problem of this paper is solved using the hybrid gravitational search and pattern search (GSA-PS) algorithms. Simulation confirm the efficiency of the GSA-PS technique compared with conventional schemes. The results also show that the generation costs of the GSA-PS are considerably reduced than the classical optimization algorithms. |
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| AbstractList | •Considering renewable energy resources.•Reporting superior solutions in the case of cost and execution time•Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management•Presenting Hybrid Gravitational Search and Pattern Search Algorithm
The use of plug-in hybrid electric vehicles (PHEVs) can solve many environmental problems and energy crises around the world. Using a large number of PHEVs with high storage and control capabilities can enhance the flexibility of distribution networks. However, optimal management PHEVs with the presence of renewable energy sources (RESs) is one of the main challenges that must be addressed. The optimal management of various RESs along with PHEVs is possible in the form of a microgrid (MG). Moreover, the uncertainties of input parameters are successfully considered in the model development by Monte Carlo simulation (MCS). In the modelling, the uncertainties in PHEVs, load, RESs and energy price are considered and simulated for 24 h. The NiMH-Battery is also used to investigate the role of the storage device. The objective function is minimizing the total cost of the grid-connected MG including the costs of load supply, PHEVs charging demand and power losses. The optimization problem of this paper is solved using the hybrid gravitational search and pattern search (GSA-PS) algorithms. Simulation confirm the efficiency of the GSA-PS technique compared with conventional schemes. The results also show that the generation costs of the GSA-PS are considerably reduced than the classical optimization algorithms. |
| ArticleNumber | 103279 |
| Author | Li, Ning Su, Zhanguo Jerbi, Houssem Latifi, Mohsen Abbassi, Rabeh Furukawa, Noritoshi |
| Author_xml | – sequence: 1 givenname: Ning surname: Li fullname: Li, Ning organization: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an,710048, China – sequence: 2 givenname: Zhanguo surname: Su fullname: Su, Zhanguo email: suzhanguo@163.com organization: Faculty of Physical Education, HuaiNan Normal University, Huainan, China – sequence: 3 givenname: Houssem surname: Jerbi fullname: Jerbi, Houssem organization: University of Ha'il, College of Engineering, Department of Industrial Engineering, Hail 1234, KSA – sequence: 4 givenname: Rabeh surname: Abbassi fullname: Abbassi, Rabeh organization: University of Ha'il, College of Engineering, Department of Electrical Engineering, Hail 1234, KSA – sequence: 5 givenname: Mohsen surname: Latifi fullname: Latifi, Mohsen organization: Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran – sequence: 6 givenname: Noritoshi surname: Furukawa fullname: Furukawa, Noritoshi organization: Solar Energy and Power Electronic Co., Ltd, Tokyo, Japan |
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| Cites_doi | 10.1016/j.jclepro.2014.09.088 10.1007/s11047-009-9175-3 10.1016/j.scs.2021.102946 10.1016/j.energy.2012.02.007 10.1016/j.scs.2018.09.002 10.1137/S1052623400374495 10.1016/j.energy.2013.12.009 10.1016/j.energy.2013.01.025 10.1016/j.neucom.2013.01.027 10.1016/j.tej.2010.03.004 10.1016/j.scs.2018.05.055 10.1016/j.energy.2011.09.017 10.1109/TIE.2006.888766 10.1016/j.scs.2020.102183 10.1109/TSTE.2013.2279837 10.1016/j.energy.2013.04.048 10.1016/j.scs.2021.102852 10.1109/TSTE.2013.2264498 10.1002/er.6198 10.1016/j.scs.2020.102681 10.1109/TPWRS.2010.2057456 10.1109/TII.2015.2395957 10.1016/j.rser.2011.10.002 10.1016/j.scs.2018.01.032 10.1109/TIE.2012.2188873 |
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