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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/38189/1/38189.pdf http://psasir.upm.edu.my/id/eprint/38189/ https://www.mdpi.com/2227-9717/8/1/41 |
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| Summary: | 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 ( AWPSOcf) and sigmoid function PSO (SFPSOcf) , 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 AW/SFPSOcf methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of SFPSOcf 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 AW/SFPSOcf methods is verified by using the iterative method. |
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