| dc.description.abstract | Global warming is a popular topic and has drawn widespread attention all over the world,
because it gradually affects people’s normal life. In the long term, carbon capture and storage (CCS) technology is a promising choice to reduce COv2 emissions efficiently. However, for the fossil fuel power plants, current capture technologies are highly energy intensive and need almost one-third of the electricity generated by the power plant itself. Thus, although showing great potential for environmental benefits, the carbon capture and storage (CCS) technologies have not been applied widely and commercially successful.
Flexible carbon capture technologies, especially with solvent storage, can improve the net power output by reducing the loads of carbon capture systems and capture less COv2 when the electricity demand and prices are high. Then it will increase the loads of carbon capture systems and capture more COv2 in order to make the total COv2 emissions less than the baselines when electricity demand and prices are relatively low. During the scheduling of COv2 capture power plants (CCPPs), if the operators can consider the uncertainties of electricity prices in different periods, they will improve the scheduling performance based on the nominal values of electricity price.
In this project, a flexible carbon capture operation that changes its production capacity depending on the changes in electricity prices will be performed, incorporating with the bounded and symmetric uncertainty of electricity price by using the robust optimization. Furthermore, a Mixed Integer Nonlinear Programming (MINLP) model will be proposed to maximize the profit in CCPPs, referring the data of the past operation and electricity prices. Finally, the comparison between scheduling with the nominal value of electricity price and with different uncertainty levels will be shown in case study, and the relative optimal output schedules of the power plant under different uncertainty levels of electricity price will be made by Matlab. | en |
