Comprehensive techno-economic optimization and performance analysis of molten salt concentrated solar power tower plants in Algeria
Abstract This paper presents a comprehensive techno-economic analysis of three molten salt Concentrated Solar Power (CSP) tower plants located in the regions of Mechria, Adrar, and Tindouf in Algeria. The study evaluates the thermal efficiency, economic feasibility, and performance of these CSP usin...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97236-4 |
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| Summary: | Abstract This paper presents a comprehensive techno-economic analysis of three molten salt Concentrated Solar Power (CSP) tower plants located in the regions of Mechria, Adrar, and Tindouf in Algeria. The study evaluates the thermal efficiency, economic feasibility, and performance of these CSP using the System Advisor Model (SAM) software, which accurately models Direct Normal Irradiance (DNI), a critical factor influencing plant performance. Key parameters analyzed include Solar Multiple (SM), Thermal energy storage (TES) hours, capacity factor (CF), and the Levelized Cost of Energy (LCOE). The results demonstrate that an optimal heliostat field configuration with a SM of 1.8 and 10 h of TES achieves a capacity factor of 51.49%, with a minimum LCOE of 0.097 $/kWh. In Mechria, with operational and maintenance costs projected at 2.51 million dollars. For the Adrar region, a SM of 1.6 and TES of 2 h yield an LCOE of 0.18 $/kWh at a capacity factor of 24.03%. Similarly, in Tindouf, a SM of 1.6 and TES of 8 h result in a capacity factor of 18.95% and an LCOE of 0.17 $/kWh. The analysis reveals that the design of CSP systems, particularly the combination of solar Multiple and TES, plays a pivotal role in optimizing the economic performance of the plants, This approach enables researchers to save time and costs by using satellite-derived DNI estimations, enhancing data accuracy and optimizing CSP deployment. |
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| ISSN: | 2045-2322 |