Carbon capture and storage (CCS) is one of the key technologies to mitigate greenhouse gas (GHG) emissions from stationary sources such as power plants. However, retrofitting power plants for carbon capture (CC) entails major capital costs as well as a reduction of thermal efficiency and power output. Thus, it is essential for planning purposes to implement the minimal extent of CC retrofit in order to meet the energy requirement and grid-wide carbon emission targets. Recently proposed pinch-based techniques provide good insights for setting the minimum retrofit targets; however these techniques suffer with the limitation of simplification. This paper presents an optimization-based automated targeting model (ATM) for carbon-constrained energy planning (CCEP), focusing on the deployment of CCS. The ATM incorporates the advantages of insight-based pinch techniques and mathematical optimization approach. The applicability of ATM is demonstrated through planning at the sectoral level, as well as discrete selection of power plant units for CCS deployment. Furthermore, this approach also allows CCS to be integrated not only via retrofit but through new plants that are capture-ready at the outset; in particular, we consider the case of new bioenergy CCS (BECCS) which contributes negative emissions to a system. Hypothetical but representative case studies are solved to illustrate the proposed methodology. In particular, the results show that ATM provides good insights apart from identifying the minimum extent of retrofit. The identification of pinch point serves as a reference in which CC retrofit is justified.
ASJC Scopus subject areas
- Chemical Engineering(all)
- Industrial and Manufacturing Engineering