A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm
•A novel MO-WDO method to optimally size a standalone PV system is proposed.•LSTM model is proposed to predict the performance of a PV module.•An explicit battery model is utilized to express its dynamic behaviour.•Well formulated multi-objective functions are utilized.•Sizing ratios for the PV syst...
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| Published in: | Energy conversion and management Vol. 238; p. 114179 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Elsevier Ltd
15.06.2021
Elsevier Science Ltd |
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| ISSN: | 0196-8904, 1879-2227 |
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| Abstract | •A novel MO-WDO method to optimally size a standalone PV system is proposed.•LSTM model is proposed to predict the performance of a PV module.•An explicit battery model is utilized to express its dynamic behaviour.•Well formulated multi-objective functions are utilized.•Sizing ratios for the PV system components for general MNBSs are derived.
A new multi-objective wind driven optimization algorithm is proposed to size a standalone photovoltaic system’s components to meet the load demand for a mobile network base station at a 1% loss of load probability or less with a minimum annual total life cost. To improve the sized model’s accuracy, a long short-term memory deep learning model is utilized to forecast the hourly performance of a photovoltaic module. The long-term memory model’s performance is compared with those obtained by a linear photovoltaic model and an artificial neural network model. The comparison is carried out based on the values of normalized root mean square error, normalized mean bias error, mean absolute percentage error, and the training and testing time. Accordingly, on the values obtained for these statistical errors, the long short-term memory model outperforms better than the linear model and the artificial neural network model based. In addition, a dynamic battery model is utilized to characterize the dynamic charging and discharging process. The findings show that the optimal number of the photovoltaic array and the capacity of the storage battery required to cover the load demand of a mobile network base station are 5.4 kWp and 2640 Ah/48 V, respectively. Besides, the annual total life cycle cost for the sized photovoltaic/battery configuration is 4028.33 AUD/year. The simulation time for the proposed method is 421.25 s. To generalize the sizing results for the mobile network base stations based on Sydney weather conditions, the photovoltaic array and storage battery ratios are calculated as 0.324 and 0.223, respectively. In addition, the cost of an energy unit generated by the optimized system is 0.254 AUD/kWh. Here, the results of the proposed method have been compared with those obtained by developed and recent benchmark published methods. The comparison outcomes show the effectiveness of the proposed method in terms of providing a high availability sized system at minimum cost within less simulation time than the other considered methods. |
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| AbstractList | A new multi-objective wind driven optimization algorithm is proposed to size a standalone photovoltaic system’s components to meet the load demand for a mobile network base station at a 1% loss of load probability or less with a minimum annual total life cost. To improve the sized model’s accuracy, a long short-term memory deep learning model is utilized to forecast the hourly performance of a photovoltaic module. The long-term memory model’s performance is compared with those obtained by a linear photovoltaic model and an artificial neural network model. The comparison is carried out based on the values of normalized root mean square error, normalized mean bias error, mean absolute percentage error, and the training and testing time. Accordingly, on the values obtained for these statistical errors, the long short-term memory model outperforms better than the linear model and the artificial neural network model based. In addition, a dynamic battery model is utilized to characterize the dynamic charging and discharging process. The findings show that the optimal number of the photovoltaic array and the capacity of the storage battery required to cover the load demand of a mobile network base station are 5.4 kWp and 2640 Ah/48 V, respectively. Besides, the annual total life cycle cost for the sized photovoltaic/battery configuration is 4028.33 AUD/year. The simulation time for the proposed method is 421.25 s. To generalize the sizing results for the mobile network base stations based on Sydney weather conditions, the photovoltaic array and storage battery ratios are calculated as 0.324 and 0.223, respectively. In addition, the cost of an energy unit generated by the optimized system is 0.254 AUD/kWh. Here, the results of the proposed method have been compared with those obtained by developed and recent benchmark published methods. The comparison outcomes show the effectiveness of the proposed method in terms of providing a high availability sized system at minimum cost within less simulation time than the other considered methods. •A novel MO-WDO method to optimally size a standalone PV system is proposed.•LSTM model is proposed to predict the performance of a PV module.•An explicit battery model is utilized to express its dynamic behaviour.•Well formulated multi-objective functions are utilized.•Sizing ratios for the PV system components for general MNBSs are derived. A new multi-objective wind driven optimization algorithm is proposed to size a standalone photovoltaic system’s components to meet the load demand for a mobile network base station at a 1% loss of load probability or less with a minimum annual total life cost. To improve the sized model’s accuracy, a long short-term memory deep learning model is utilized to forecast the hourly performance of a photovoltaic module. The long-term memory model’s performance is compared with those obtained by a linear photovoltaic model and an artificial neural network model. The comparison is carried out based on the values of normalized root mean square error, normalized mean bias error, mean absolute percentage error, and the training and testing time. Accordingly, on the values obtained for these statistical errors, the long short-term memory model outperforms better than the linear model and the artificial neural network model based. In addition, a dynamic battery model is utilized to characterize the dynamic charging and discharging process. The findings show that the optimal number of the photovoltaic array and the capacity of the storage battery required to cover the load demand of a mobile network base station are 5.4 kWp and 2640 Ah/48 V, respectively. Besides, the annual total life cycle cost for the sized photovoltaic/battery configuration is 4028.33 AUD/year. The simulation time for the proposed method is 421.25 s. To generalize the sizing results for the mobile network base stations based on Sydney weather conditions, the photovoltaic array and storage battery ratios are calculated as 0.324 and 0.223, respectively. In addition, the cost of an energy unit generated by the optimized system is 0.254 AUD/kWh. Here, the results of the proposed method have been compared with those obtained by developed and recent benchmark published methods. The comparison outcomes show the effectiveness of the proposed method in terms of providing a high availability sized system at minimum cost within less simulation time than the other considered methods. |
| ArticleNumber | 114179 |
| Author | Ibrahim, Ibrahim Anwar Sabah, Slaiman Hossain, M.J. Fahed, Hani Abbas, Robert |
| Author_xml | – sequence: 1 givenname: Ibrahim Anwar surname: Ibrahim fullname: Ibrahim, Ibrahim Anwar email: ibrahim.a.ibrahim@hdr.mq.edu.au, ibrahim.ibrahim@csiro.au, ibrahim.a.ibrahim@ieee.org organization: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia – sequence: 2 givenname: Slaiman orcidid: 0000-0002-6613-5536 surname: Sabah fullname: Sabah, Slaiman email: slaiman.sabah@students.mq.edu.au organization: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia – sequence: 3 givenname: Robert surname: Abbas fullname: Abbas, Robert email: robert.abbas@mq.edu.au organization: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia – sequence: 4 givenname: M.J. surname: Hossain fullname: Hossain, M.J. email: jahangir.hossain@uts.edu.au organization: School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia – sequence: 5 givenname: Hani orcidid: 0000-0001-5399-8546 surname: Fahed fullname: Fahed, Hani email: hani.fahed@gmail.com organization: Bombardier Transportation, Melbourne, VIC 3000, Australia |
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| Keywords | Deep learning Photovoltaic (PV) Multi-objective optimization Mobile network base station Standalone PV system |
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| SubjectTerms | administrative management Algorithms Arrays Artificial neural networks Batteries Computer simulation Deep learning energy conversion Errors life cycle costing Life cycle costs Life cycles linear models Long short-term memory Long term memory Machine learning memory Minimum cost Mobile network base station Model accuracy Multi-objective optimization Multiple objective analysis Neural networks Optimization Optimization algorithms Photovoltaic (PV) Photovoltaic cells Photovoltaics probability Radio equipment Sizing solar collectors Standalone PV system Statistical analysis Testing time Weather Wind |
| Title | A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm |
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