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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hussein Mohammed Ridha [verfasserIn] Chandima Gomes [verfasserIn] Hashim Hizam [verfasserIn] Masoud Ahmadipour [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	standalone pv system multi-objective optimization particle swarm optimization life cycle cost (lcc) loss of load probability (llp) levelized cost of energy (lce)

Übergeordnetes Werk:	In: Processes - MDPI AG, 2013, 8(2020), 1, p 41
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:1, p 41

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pr8010041

Katalog-ID:	DOAJ025327321

Internformat


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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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700	0		\|a Hashim Hizam \|e verfasserin \|4 aut
700	0		\|a Masoud Ahmadipour \|e verfasserin \|4 aut
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allfields	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
spelling	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
allfieldsGer	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
allfieldsSound	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
language	English
source	In Processes 8(2020), 1, p 41 volume:8 year:2020 number:1, p 41
sourceStr	In Processes 8(2020), 1, p 41 volume:8 year:2020 number:1, p 41
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	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
isfreeaccess_bool	true
container_title	Processes
authorswithroles_txt_mv	Hussein Mohammed Ridha @@aut@@ Chandima Gomes @@aut@@ Hashim Hizam @@aut@@ Masoud Ahmadipour @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	750371439
id	DOAJ025327321
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ025327321</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307084614.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/pr8010041</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ025327321</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6571847596894b98906da3ec41b86314</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP1-1185</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QD1-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hussein Mohammed Ridha</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">standalone pv system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-objective optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">particle swarm optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">life cycle cost (lcc)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">loss of load probability (llp)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">levelized cost of energy (lce)</subfield></datafield><datafield tag="653" 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callnumber-first	T - Technology
author	Hussein Mohammed Ridha
spellingShingle	Hussein Mohammed Ridha misc TP1-1185 misc QD1-999 misc standalone pv system misc multi-objective optimization misc particle swarm optimization misc life cycle cost (lcc) misc loss of load probability (llp) misc levelized cost of energy (lce) misc Chemical technology misc Chemistry Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
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topic_title	TP1-1185 QD1-999 Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia standalone pv system multi-objective optimization particle swarm optimization life cycle cost (lcc) loss of load probability (llp) levelized cost of energy (lce)
topic	misc TP1-1185 misc QD1-999 misc standalone pv system misc multi-objective optimization misc particle swarm optimization misc life cycle cost (lcc) misc loss of load probability (llp) misc levelized cost of energy (lce) misc Chemical technology misc Chemistry
topic_unstemmed	misc TP1-1185 misc QD1-999 misc standalone pv system misc multi-objective optimization misc particle swarm optimization misc life cycle cost (lcc) misc loss of load probability (llp) misc levelized cost of energy (lce) misc Chemical technology misc Chemistry
topic_browse	misc TP1-1185 misc QD1-999 misc standalone pv system misc multi-objective optimization misc particle swarm optimization misc life cycle cost (lcc) misc loss of load probability (llp) misc levelized cost of energy (lce) misc Chemical technology misc Chemistry
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title	Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
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title_full	Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
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author_browse	Hussein Mohammed Ridha Chandima Gomes Hashim Hizam Masoud Ahmadipour
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title_sort	optimal design of standalone photovoltaic system based on multi-objective particle swarm optimization: a case study of malaysia
callnumber	TP1-1185
title_auth	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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title_short	Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
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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. 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