Abstract
This paper aims to assess the impact of different key factors on the optimized design and performance of grid connected photovoltaic (PV) power plants, as such key factors can lead to re-design the PV plant and affect its optimum performance. The impact on the optimized design and performance of the PV plant is achieved by considering each factor individually. A comprehensive analysis is conducted on nine factors such as; three objectives are predefined, five recent optimization approaches, three different locations around the world, changes in solar irradiance, ambient temperature, and wind speed levels, variation in the available area, PV module type and inverters size. The performance of the PV plant is evaluated for each factor based on five performance parameters such as; energy yield, sizing ratio, performance ratio, ground cover ratio, and energy losses. The results show that the geographic location, a change in meteorological conditions levels, and an increase or decrease in the available area require the re-design of the PV plant. A change in inverter size and PV module type has a significant impact on the configuration of the PV plant leading to an increase in the cost of energy. The predefined objectives and proposed optimization methods can affect the PV plant design by producing completely different structures. Furthermore, most PV plant performance parameters are significantly changed due to the variation of these factors. The results also show the environmental benefit of the PV plant and the great potential to avoid green-house gas emissions from the atmosphere.
Highlights
At present, large-scale photovoltaic (PV) power plants represent the highest rate of power investments compared to conventional power generation and renewable energy sources such as wind power
The cost of PV power plants is promising due to the intrinsic qualities of the system compared to other renewable energy sources, limited maintenance requirements, reduced service costs, reliable, noiseless, and easy to install [2]
The grey wolf optimizer-sine cosine algorithm was applied for this purpose, and the results showed that it was possible to obtain good solutions with this approach
Summary
Large-scale photovoltaic (PV) power plants represent the highest rate of power investments compared to conventional power generation and renewable energy sources such as wind power. The penetration rate of large-scale PV power plants is growing quickly, including small PV power plants [1]. The cost of PV power plants is promising due to the intrinsic qualities of the system compared to other renewable energy sources, limited maintenance requirements, reduced service costs, reliable, noiseless, and easy to install [2]. There is continuous improvement in the conversion efficiency for crystalline silicon (c-Si) and thin-film cadmium telluride (CdTe) PV modules [3], [4]. In 2019, the photovoltaic energy increased with a capacity of around 115 gigawatts. The statistics show that the PV market increased from a capacity
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