The hybrid MPC-MINLP algorithm for optimal operation of coal-fired power plants with solvent based post-combustion CO2 capture

- Algorithm for optimal operation of coal-fired power plants with carbon capture.
- Algorithm combines model predictive control (MPC) with MINLP optimization.
- Plant revenue maximized under actual electricity prices and fixed carbon prices.
- Flexible operation improves revenue by 6% over 'fixed' operation in a 24 h period.
- Presented multi-level control-optimization framework represents competitive asset.

This paper presents an algorithm that combines model predictive control (MPC) with MINLP optimization and demonstrates its application for coal-fired power plants retrofitted with solvent based post-combustion CO2 capture (PCC) plant. The objective function of the optimization algorithm works at a primary level to maximize plant economic revenue while considering an optimal carbon capture profile. At a secondary level, the MPC algorithm is used to control the performance of the PCC plant. Two techno-economic scenarios based on fixed (capture rate is constant) and flexible (capture rate is variable) operation modes are developed using actual electricity prices (2011) with fixed carbon prices ($AUD 5, 25, 50/tonne-CO2) for 24 h periods. Results show that fixed operation mode can bring about a ratio of net operating revenue deficit at an average of 6% against the superior flexible operation mode.