研究目的
To determine the most adapted photovoltaic module technology for the desert climate of Errachidia, Morocco, by analyzing the long-term performance, degradation, and cost analysis of three different PV module technologies.
研究成果
The study concludes that p-Si technology exhibits the best performance with the highest PR of 82.54 ± 5.84 % but degrades faster with 0.92 ± 0.11 %/year. The m-Si technology shows the lowest degradation rate of 0.45 ± 0.11 %/year. The LCOE analysis reveals that p-Si is the most cost-effective solution with 10.32 c€/kWh, making it the most suitable for the arid climate of Morocco.
研究不足
The study is limited to the arid climate of Errachidia, Morocco, and may not be directly applicable to other climatic conditions. The performance and degradation rates are based on almost three years of data, which may not fully represent the long-term behavior over the typical 25-year lifespan of PV modules.
1：Experimental Design and Method Selection:
The study involved the installation of three different PV module technologies (m-Si, p-Si, and a-Si) in the desert climate of Errachidia, Morocco. The performance ratio (PR), degradation rate (Rd), and levelized cost of electricity (LCOE) were calculated for each technology.
2：Sample Selection and Data Sources:
The PV systems were installed on the roof of the Faculty of Sciences and Techniques Errachidia. Data on generated energy and meteorological conditions were collected over almost three years.
3：List of Experimental Equipment and Materials:
The study used PV modules from different manufacturers, SMA solar inverters, and a Davis Weather Vantage Pro2 meteorological station.
4：Experimental Procedures and Operational Workflow:
The generated energy by each PV system was recorded every 5 minutes. Daily generated energy was checked to prevent days with empty or uncorrected data.
5：Data Analysis Methods:
The performance indicators (PR, Yf, Yr) were calculated according to IEC 61724 and IEA-PVPS task 13 standards. The degradation rate was calculated using the performance ratio regression on a monthly basis.
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