Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of en...
Ausführliche Beschreibung
Autor*in: |
Hussein Mohammed Ridha [verfasserIn] Chandima Gomes [verfasserIn] Hashim Hizam [verfasserIn] Masoud Ahmadipour [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
In: Processes - MDPI AG, 2013, 8(2020), 1, p 41 |
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Übergeordnetes Werk: |
volume:8 ; year:2020 ; number:1, p 41 |
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DOI / URN: |
10.3390/pr8010041 |
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Katalog-ID: |
DOAJ025327321 |
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520 | |a This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. | ||
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10.3390/pr8010041 doi (DE-627)DOAJ025327321 (DE-599)DOAJ6571847596894b98906da3ec41b86314 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Hussein Mohammed Ridha verfasserin aut Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. standalone pv system multi-objective optimization particle swarm optimization life cycle cost (lcc) loss of load probability (llp) levelized cost of energy (lce) Chemical technology Chemistry Chandima Gomes verfasserin aut Hashim Hizam verfasserin aut Masoud Ahmadipour verfasserin aut In Processes MDPI AG, 2013 8(2020), 1, p 41 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:8 year:2020 number:1, p 41 https://doi.org/10.3390/pr8010041 kostenfrei https://doaj.org/article/6571847596894b98906da3ec41b86314 kostenfrei https://www.mdpi.com/2227-9717/8/1/41 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1, p 41 |
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10.3390/pr8010041 doi (DE-627)DOAJ025327321 (DE-599)DOAJ6571847596894b98906da3ec41b86314 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Hussein Mohammed Ridha verfasserin aut Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. standalone pv system multi-objective optimization particle swarm optimization life cycle cost (lcc) loss of load probability (llp) levelized cost of energy (lce) Chemical technology Chemistry Chandima Gomes verfasserin aut Hashim Hizam verfasserin aut Masoud Ahmadipour verfasserin aut In Processes MDPI AG, 2013 8(2020), 1, p 41 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:8 year:2020 number:1, p 41 https://doi.org/10.3390/pr8010041 kostenfrei https://doaj.org/article/6571847596894b98906da3ec41b86314 kostenfrei https://www.mdpi.com/2227-9717/8/1/41 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1, p 41 |
allfields_unstemmed |
10.3390/pr8010041 doi (DE-627)DOAJ025327321 (DE-599)DOAJ6571847596894b98906da3ec41b86314 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Hussein Mohammed Ridha verfasserin aut Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. standalone pv system multi-objective optimization particle swarm optimization life cycle cost (lcc) loss of load probability (llp) levelized cost of energy (lce) Chemical technology Chemistry Chandima Gomes verfasserin aut Hashim Hizam verfasserin aut Masoud Ahmadipour verfasserin aut In Processes MDPI AG, 2013 8(2020), 1, p 41 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:8 year:2020 number:1, p 41 https://doi.org/10.3390/pr8010041 kostenfrei https://doaj.org/article/6571847596894b98906da3ec41b86314 kostenfrei https://www.mdpi.com/2227-9717/8/1/41 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1, p 41 |
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10.3390/pr8010041 doi (DE-627)DOAJ025327321 (DE-599)DOAJ6571847596894b98906da3ec41b86314 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Hussein Mohammed Ridha verfasserin aut Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. standalone pv system multi-objective optimization particle swarm optimization life cycle cost (lcc) loss of load probability (llp) levelized cost of energy (lce) Chemical technology Chemistry Chandima Gomes verfasserin aut Hashim Hizam verfasserin aut Masoud Ahmadipour verfasserin aut In Processes MDPI AG, 2013 8(2020), 1, p 41 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:8 year:2020 number:1, p 41 https://doi.org/10.3390/pr8010041 kostenfrei https://doaj.org/article/6571847596894b98906da3ec41b86314 kostenfrei https://www.mdpi.com/2227-9717/8/1/41 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1, p 41 |
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10.3390/pr8010041 doi (DE-627)DOAJ025327321 (DE-599)DOAJ6571847596894b98906da3ec41b86314 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Hussein Mohammed Ridha verfasserin aut Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. standalone pv system multi-objective optimization particle swarm optimization life cycle cost (lcc) loss of load probability (llp) levelized cost of energy (lce) Chemical technology Chemistry Chandima Gomes verfasserin aut Hashim Hizam verfasserin aut Masoud Ahmadipour verfasserin aut In Processes MDPI AG, 2013 8(2020), 1, p 41 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:8 year:2020 number:1, p 41 https://doi.org/10.3390/pr8010041 kostenfrei https://doaj.org/article/6571847596894b98906da3ec41b86314 kostenfrei https://www.mdpi.com/2227-9717/8/1/41 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1, p 41 |
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Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia |
abstract |
This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. |
abstractGer |
This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. |
abstract_unstemmed |
This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (<inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula< and sigmoid function PSO <inline-formula< <math display="inline"< <semantics< <mrow< <mo stretchy="false"<(</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< <mo stretchy="false"<)</mo< </mrow< </semantics< </math< </inline-formula<, are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mi<A</mi< <mi<W</mi< <mo</</mo< <mi<S</mi< <mi<F</mi< <mi<P</mi< <mi<S</mi< <msub< <mi<O</mi< <mrow< <mi<c</mi< <mi<f</mi< </mrow< </msub< </mrow< </semantics< </math< </inline-formula< methods is verified by using the iterative method. |
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Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia |
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