Crisp and fuzzy integer programming models for optimal carbon sequestration retrofit in the power sector

Carbon capture and storage (CCS) is one of the interim technologies to mitigate greenhouse gas emissions from stationary sources such as power plant and large industrial facilities. CCS allows for continued utilization of fossil fuels (e.g. coal, natural gas and oil), which are still relatively inexpensive and reliable in comparison to inherently low-carbon renewable resources (e.g. wind, solar etc.). On the other hand, retrofitting power plants for carbon capture (CC) entails major capital costs as well as reduction of thermal efficiency and power output. This paper presents integer programming optimization models for planning the retrofit of power plants at the regional, sectoral or national level. In addition to the base case (i.e., non-fuzzy or crisp) formulation, two fuzzy extensions are given to account for the inherent conflict between environmental and economic goals, as well as parametric uncertainties pertaining to the emerging CC technologies. Case studies are shown to illustrate the modeling approach.