Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system
The paper presents a new multiple scenario multi-objective salp swarm optimization (MS-MOSS) algorithm to optimally size a standalone PV system. An accurate estimation of the number of PV modules and storage battery is crucial as it affects the system reliability and cost. Three scenarios have been...
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| Published in | Renewable energy Vol. 153; pp. 1330 - 1345 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.06.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0960-1481 1879-0682 |
| DOI | 10.1016/j.renene.2020.02.016 |
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| Abstract | The paper presents a new multiple scenario multi-objective salp swarm optimization (MS-MOSS) algorithm to optimally size a standalone PV system. An accurate estimation of the number of PV modules and storage battery is crucial as it affects the system reliability and cost. Three scenarios have been presented focusing on Pareto optimal solutions by minimizing two conflicting objectives. Loss of load probability (LLP) and life-cycle cost (LLC) are considered to obtain the Pareto front. The iterative method is employed for validation of the superiority results of the proposed MS-MOSS algorithm. The results show that the scenarios are able to find Pareto optimal configuration at a high level of accuracy and at a very low cost. The proposed three scenarios are faster than iterative approach approximately by 158, 194.2, and 141.6 times, respectively. The third scenario outperforms other scenarios in terms of coverage and convergence of the distribution of solution to the Pareto front. As a conclusion, The MS-MOSS algorithm is found to be very effective in sizing of SAPV system.
•A new multiple scenario multi-objective salp swarm optimization (MS-MOSS) is proposed.•An optimal size for a standalone PV system is found using MS-MOSS.•Three scenarios have been presented to obtain on Pareto optimal solutions.•The results demonstrate the efficiency of MS-MOSS for sizing of SAPV system. |
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| AbstractList | The paper presents a new multiple scenario multi-objective salp swarm optimization (MS-MOSS) algorithm to optimally size a standalone PV system. An accurate estimation of the number of PV modules and storage battery is crucial as it affects the system reliability and cost. Three scenarios have been presented focusing on Pareto optimal solutions by minimizing two conflicting objectives. Loss of load probability (LLP) and life-cycle cost (LLC) are considered to obtain the Pareto front. The iterative method is employed for validation of the superiority results of the proposed MS-MOSS algorithm. The results show that the scenarios are able to find Pareto optimal configuration at a high level of accuracy and at a very low cost. The proposed three scenarios are faster than iterative approach approximately by 158, 194.2, and 141.6 times, respectively. The third scenario outperforms other scenarios in terms of coverage and convergence of the distribution of solution to the Pareto front. As a conclusion, The MS-MOSS algorithm is found to be very effective in sizing of SAPV system.
•A new multiple scenario multi-objective salp swarm optimization (MS-MOSS) is proposed.•An optimal size for a standalone PV system is found using MS-MOSS.•Three scenarios have been presented to obtain on Pareto optimal solutions.•The results demonstrate the efficiency of MS-MOSS for sizing of SAPV system. The paper presents a new multiple scenario multi-objective salp swarm optimization (MS-MOSS) algorithm to optimally size a standalone PV system. An accurate estimation of the number of PV modules and storage battery is crucial as it affects the system reliability and cost. Three scenarios have been presented focusing on Pareto optimal solutions by minimizing two conflicting objectives. Loss of load probability (LLP) and life-cycle cost (LLC) are considered to obtain the Pareto front. The iterative method is employed for validation of the superiority results of the proposed MS-MOSS algorithm. The results show that the scenarios are able to find Pareto optimal configuration at a high level of accuracy and at a very low cost. The proposed three scenarios are faster than iterative approach approximately by 158, 194.2, and 141.6 times, respectively. The third scenario outperforms other scenarios in terms of coverage and convergence of the distribution of solution to the Pareto front. As a conclusion, The MS-MOSS algorithm is found to be very effective in sizing of SAPV system. |
| Author | Ridha, Hussein Mohammed Gomes, Chandima Hizam, Hashim Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Hussein Mohammed surname: Ridha fullname: Ridha, Hussein Mohammed email: gs46648@student.upm.edu.my, hussain_mhammad@yahoo.com organization: Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia – sequence: 2 givenname: Chandima surname: Gomes fullname: Gomes, Chandima email: chandima.gomes@wits.ac.za organization: School of Electrical and Information Engineering, University of Witwatersrand, 1 Jan Smuts Avenue, Braamfontein, Johannesburg, 2000, South Africa – sequence: 3 givenname: Hashim surname: Hizam fullname: Hizam, Hashim email: hhizam@upm.edu.my organization: Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia – sequence: 4 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali email: ali.mirjalili@gmail.com organization: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, 4006, QLD, Australia |
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| SubjectTerms | algorithms batteries LCC life cycle costing LLP Multi-objectives optimization Multiple scenarios probability renewable energy sources Salp swarm algorithm solar collectors Standalone PV system |
| Title | Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system |
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