Fixed and Capture Level Reduction operating modes for carbon dioxide removal in a Natural Gas Combined Cycle power plant

摘要

The characteristics (flowrate, composition, CO2 partial pressure) of the flue gas exiting a Natural Gas Combined Cycle (NGCC) power plant are different from the ones of coal-fired units on which analyses in the literature are usually focusing, therefore for application of Carbon Capture and Storage (CCS) to the removal of CO2 from this type of stream a detailed study is needed. This work applies the method of flexible operation aimed at reducing the economic disadvantages of a CCS plant, and considers the Capture Level Reduction (CLR) mode for the carbon dioxide removal section of a NGCC power plant located in Italy, of which type the number of units is constantly increasing. The CLR mode consists in emitting part of the carbon dioxide at given times during the day by bypassing part of the rich solvent and thus reducing the energy requirements for regeneration of the solvent and compression of the removed CO2. For the study a proprietary code developed by the GASP group of Politecnico di Milano, based on real energy prices and demand and taking into account different values of possible carbon taxes, has been employed. The results show that the best load to the absorption section strongly depends on the value of the considered carbon tax, in addition to the time-dependent request and price of electricity. A carbon tax of 5 (sic)/tonCO(2) generally does not favor the operation of the CCS system, while higher values exert a more significant influence on the revenues. Carbon taxes of 115 (sic)/tonCO(2) for the winter period and of 145 (sic)/tonCO(2) for the summer period make the operation of the CCS section more economically advantageous than emitting carbon dioxide to the atmosphere. In any case, also when applied to CO2 removal in power plants fed by natural gas, the CLR mode results more advantageous than fixed operation, thus making power plants companies increasing their efforts in reducing greenhouse gas emissions. (C) 2020 Elsevier Ltd. All rights reserved.