Predicting the amount of solar radiation that reaches the earth’s surface is critical to understanding the performance of solar power systems, and cloud cover has a particularly strong impact on both the amount and direction of this radiation. Due to its variable nature, solar power is typically thought of as able to provide electricity only as a supplement to traditional power sources. However, by incorporating energy storage into solar facility design, it is possible to mitigate the variations in power production due to changes in sunlight. A key question then is how much energy storage would be required to account for daily solar irradiance variations and allow a solar power facility to produce electricity at least 80% of the year, comparable to traditional coal and natural gas plants. I have developed a simple algorithm for computing the intensity and angular distribution of light transmitted through, and reflected from, clouds. This result allows for accurate determination of variations in irradiance values across the globe. I have also created a model for the energy produced from a 100MW(e) solar power facility coupled to a large-scale thermal energy storage system. I used daily solar irradiance values to determine the array size needed at every location on the planet, and compared the power output at every location when both 1200MWh(e) and 1800MWh(e) of storage were incorporated into the plant design. I then computed the fraction of the year that power was produced at the rated capacity and the amount of time before the facility energy requirements are recouped. My analysis shows that more than 69% of the global land mass has sufficient solar resources provide continuous electricity output more than 80% of the time, and 27% of the land mass can do this more than 90% of time. In these locations the energy payback time ranges from 1.75 to 10 years. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/22287 |
| Date | 20 November 2013 |
| Creators | Stoll, Brady Leigh |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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