(724e) Continuous-Time Model for Retrofitting of Power Plants and Optimal Carbon-Capture and Storage

Carbon capture and storage (CCS) is one of the options for mitigating the adverse effects of CO₂ emissions and the resulting greenhouse gas and climate change effects. CCS involves a two-step process of directly capturing CO₂ from different sources (such as flue gas emissions of power plants), using one of the carbon capture technologies, and then transporting and storing CO₂ in natural sinks (such as geological reservoirs or exhausted oil/gas wells). The different carbon capture technologies include pre-combustion capture in integrated gasification combined cycle (IGCC), post-combustion capture based on flue gas scrubbing, oxy-fuel combustion, and chemical looping combustion (CLC). Other alternatives for reducing CO₂ emissions include improving energy efficiency of power plants or using low-carbon fuel technologies [1-4].

CCS is essential for continued use of fossil fuel based technologies to mitigate the environmental concerns related to carbon footprint. Several approaches based on pinch analysis and mathematical programming have been developed in the literature for source-sink matching of CO₂ emissions. All the three operations: carbon capture, transportation, and storage consume some energy to perform them, again resulting in additional CO₂ emissions. On one hand we are capturing CO₂ and storing into allocated geological sink, while on the other hand we are allowing additional CO₂ emission into atmosphere in order to ensure feasibility of whole process, leading to compensatory power generation in case of power plants. Enabling of CCS requires retrofitting of existing power plants to allow for capturing of CO₂ in pure form, thus reducing the power output of these plants, which are modeled in the literature as energy penalties.

Tan et al [2] proposed a CO₂ source-sink continuous time mixed integer nonlinear programming (MINLP) model considering physical and temporal effects of planning CCS. The temporal effects include time matching of start and end times of operating times of sources with sinks. Later they used linearization of bilinear terms and converted the model into MILP. Fixed operating lives and fixed flow rates were considered for CO₂ sources. Lee and Chen [3] presented an improved version of Tan et al [2] model with reduction in number of variables and constraints. Lee et al [4] extended this work using discrete time intervals to include decisions related to different carbon capture technologies for retrofitting of power plants, variable flow rates for CO₂ sources, and finite injection rates for CO₂ sinks.

In this work, we present an improved version of the literature models [2-3] for optimal source-sink matching in carbon capture and storage systems, without requiring bilinear terms and the resulting linearization. Hence, it directly results in an MILP model with reduced problem size in terms of number of variables and constraints. Two case studies from literature will be presented to illustrate the proposed advantages based on these modifications.

Then, we present an MILP model for retrofitting of power plants based on different carbon capture technologies using continuous time representation. The objective is to maximize CO₂ emissions reduction by retrofitting power plants and matching CO₂ sources and sinks, while keeping electricity cost within a specified limit and satisfying all temporal and physical constraints. A case study from literature [4] was solved using the proposed continuous time MILP model, yielding orders of magnitude reduction in problem size, as expected. Additionally, we will present sensitivity analysis by varying the minimum duration of viable connection between sources and sinks to study its effect on the total CO₂ captured.

References

[1] Shenoy, A. U.; Shenoy, U. V. (2012). Targeting and design of energy allocation networks with carbon capture and storage, Chem. Eng. Sci., 68, 312â??327.

[2] Tan, R. R.; Aviso, K.B.; Bandyopadhyay, S.; Ng, D. K. S. (2012). Continuous-time optimization model for source-sink matching in carbon capture and storage systems, Ind. Eng. Chem. Res., 51, 10015â??10020.

[3] Lee, J.-Y.; Chen, C.-L. (2012). Comments on â??Continuous-time optimization model for source-sink matching in carbon capture and storage systemsâ??, Ind. Eng. Chem. Res., 51, 11590â??11591.

[4] Lee, J.-Y.; Tan, R. R.; Chen, C.-L. (2014). A unified model for the deployment of carbon capture and storage. App. Energy, 121, 140â??148.