The hybrid method based on ant colony optimization algorithm in multiple factor analysis of the environmental impact of solar cell technologies

Bo Dong; Alexey Luzin; Dmitry Gura; Bo Dong; Alexey Luzin; Dmitry Gura

doi:10.3934/mbe.2020334

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 6342-6354. doi: 10.3934/mbe.2020334

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Research article

The hybrid method based on ant colony optimization algorithm in multiple factor analysis of the environmental impact of solar cell technologies

1.
School of Innovation and Entrepreneurship, Liaoning University, Chongshan Road, 66, Shenyang, Liaoning, 110000, China
2.
Department of chemistry, Sechenov First Moscow State Medical University, Trubetskaya st, 8, Moscow, 119991, Russian Federation
3.
Department of Cadastre and Geoengineering, Kuban State Technological University, Moscow Str., 2 building B, Krasnodar, 350072, Russian Federation
4.
Department of geodesy, Kuban State Agrarian University, 13 Kalinina Str., 350044, Russian Federation

Received: 18 June 2020 Accepted: 08 September 2020 Published: 23 September 2020

The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hybrid optimization method based on particle swarm and ant colony optimization algorithms to solve the problems of PV module toxicity. The Weibull distribution function was used to measure the service life of PV modules under a variety of failure scenarios. The simulation results show that PV modules that were guaranteed to have the service life of 25–30 years mostly last 20–25 years. The toxicity coefficient and the use of a hybrid method suggest that the time period when a solar module exhibits a maximum efficiency with a minimal environmental footprint ranges from 15 to 20 years. It was established that this interval corresponds to the level at which the amount of waste does not exceed the amount of energy generated with a minimum number of failures. The proposal will be effective in predicting the performance of solar systems. This approach can be improved in terms of cost and benefit and employed in the future research on renewable energy and ecosystems.
- Ant Colony Optimization Algorithm,
- new technologies,
- Particle Swarm Optimization Algorithm,
- PV panel waste,
- solar energy
Citation: Bo Dong, Alexey Luzin, Dmitry Gura. The hybrid method based on ant colony optimization algorithm in multiple factor analysis of the environmental impact of solar cell technologies[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6342-6354. doi: 10.3934/mbe.2020334

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Abstract

The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hybrid optimization method based on particle swarm and ant colony optimization algorithms to solve the problems of PV module toxicity. The Weibull distribution function was used to measure the service life of PV modules under a variety of failure scenarios. The simulation results show that PV modules that were guaranteed to have the service life of 25–30 years mostly last 20–25 years. The toxicity coefficient and the use of a hybrid method suggest that the time period when a solar module exhibits a maximum efficiency with a minimal environmental footprint ranges from 15 to 20 years. It was established that this interval corresponds to the level at which the amount of waste does not exceed the amount of energy generated with a minimum number of failures. The proposal will be effective in predicting the performance of solar systems. This approach can be improved in terms of cost and benefit and employed in the future research on renewable energy and ecosystems.

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