Decarbonizing existing coal-fired power stations considering endogenous technology learning: a Turkish case study

Official URL: http://dx.doi.org/10.1016/j.jclepro.2020.121100

Despite environmental threats, coal is expected to remain a remarkable energy source as the share of intermittent renewable sources in the energy mix grows; hence, managing pollutants such as carbon dioxide (CO2) is critical while pushing for clean alternatives. Carbon capture, utilization, and storage (CCUS) is a set of methods that removes CO2 from emissions and stores it safely for the long-term. As it stands now, CCUS is prohibitively expensive; however, learning-by-doing is a recognized phenomenon that assists novel technologies to become more affordable as employed continuously. Thus, for accurate long-term planning, this study investigates the impact of early adopters on CCUS market diffusion considering endogenous technology learning (ETL). A mixed-integer nonlinear programming model (MINLP) is developed which determines the optimal capacity and deployment time of post-combustion carbon capture units and the optimal source–sink matching in the CO2 supply chain, taking into account techno-economic constraints and regulations. The MINLP model is transformed into a series of mixed-integer linear programming models using a Stackelberg game approach and solved with a diagonalization method. Equilibrium solutions of the proposed model were obtained for independent and coordinated actions of players. The proposed model is applied to the Turkish energy market with multiple CO2 sources and sinks, where a cap-and-trade program is presumed to be in place. While the study demonstrates the interplay between CCUS and ETL in Turkey, the proposed model is universal, and it can be applied to other countries if the inputs are available.