Forecasting for concentrated solar thermal power plants in Australia

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Copyright: Law, Edward
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Abstract
Up to 50% of electricity needs in Australia could be supplied by solar power. At these high levels of solar power generation, solar forecasting is necessary to manage the impact of solar variability. However, there has been little research on using solar forecasting in Australia. This study used modelling to investigate the benefits of using short-term and long-term solar forecasts to operate a concentrated solar thermal (CST) plant for a year at four sites that covered different climate zones within the Australian National Electricity Market. Using 1-hour ahead short-term forecasts increased net value by $0.90-$2.07 million for a CST plant with storage, and by $0.76-$3.10 million for a CST plant without storage. It also improved reliability by reducing the equivalent forced outage rate by 21-38 percentage points for a CST plant with storage, and by 16-42 percentage points for a CST plant without storage. Using 1-hour forecasts achieved 59%-94% of the net value achievable if the 48-hour forecast were perfect. At each site, the highest net value and reliability were achieved by a CST plant with storage and using 1-hour forecasts, thus a CST plant should have both storage and short-term forecasts. If only one can be used, then a CST plant with storage and without 1-hour forecasts achieves higher net value, whereas a CST plant without storage and with 1-hour forecasts achieves higher reliability. These results demonstrated that using short-term forecasts is beneficial for CST plants that operate in electricity markets that allow updated bids to be submitted at short-term time frames. The results can be used to estimate the return on investment in obtaining short-term forecasts for operating a CST plant. Furthermore, the research method can be adapted into a tool for estimating value to assist CST plant project planning.
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Author(s)
Law, Edward
Supervisor(s)
Kay, Merlinde
Taylor, Robert
Morrison, Graham
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Publication Year
2017
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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download public version.pdf 2.31 MB Adobe Portable Document Format
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