Efficiency optimization model for coal-fired power plants with CCS in emissions trading markets

To address the challenge of minimizing emission allowance purchasing costs and carbon capture system (CCS) energy consumption in coal-fired power plants (CFPPs) participating in emission trading schemes (ETS), this study develops a novel stochastic optimization framework for dynamic carbon capture efficiency (CCE) adjustment. First, a deterministic optimization model is established to quantify the price-sensitive range (PSR) of emission allowance prices (EAP), which is required to incentivize active CFPP participation in ETS while balancing CCS operational costs. Due to the inherent EAP uncertainty in real markets, a superquantile-based risk characterization of emission allowance purchasing costs is developed using Monte Carlo discretization. This leads to the creation of confidence-level-dependent operational strategies that maximize plant profitability under fluctuating electricity prices, net output requirements, and carbon market volatility. Simulation analyses demonstrate that the proposed approach provides conservative, adaptive operating modes, which reduce costs compared to deterministic methods while enhancing capture efficiency flexibility and delivering economically viable decarbonization pathways for CCS-equipped power infrastructure.