이산화탄소 포집∙저장 기반시설의 최적 설계 및 운영을 위한 수학적 모델 개발

Abstract

This thesis addresses the infrastructure planning problem for carbon capture and storage (CCS) in responding to climate change. To tackle this problem, seven mathematical programing models are suggested, with regards to their treatment of uncertainty and decision structure. Chapter I addresses mathematical modeling frameworks involving CCS infrastructure in order to obtain decision information. Chapter I.1 introduces a scalable and comprehensive CCS network model that estimates profit, provides the optimal configuration of the CCS infrastructure, and identifies optimal CCS technologies. The proposed model determines where and how much CO2 to be captured, stored, transported, sequestrated and utilized by formulating the model as an MILP problem. The model developed in Chapter I.2 extends the one examined in Chapter I.1 to include several features of great importance in the area of CCS infrastructure design and operation. These features include (i) different sizes of utilization, capture, storage, and sequestration facilities, and (ii) evolution of CO2 reduction target over a long-term planning interval. In Chapter I.3, to supplement the technical and economic limit of CCS, carbon emission trading (CET) is used as a policy based incentive, thus an optimization model is proposed for planning electricity generation and CO2 mitigation (EGCM) strategies that generate a fully integrated, profit-maximizing CCS infrastructure and CET system. In Chapter II, we propose methods to include uncertainty or variability of key parameters in the decision models for a probabilistic analysis with CCS. Chapter II.1 analyzes the effect of uncertainty in CO2 emissions by developing a two-stage stochastic MILP model. Chapter II.2 also analyzes the effect of every possible uncertainty in product prices, operating costs and CO2 emissions by developing an inexact two-stage stochastic MILP model. In Chapter III, multi-objective optimization problems as well as the uncertainty problems in a decision-making for CCS are addressed, which optimal decision structures need to be taken in the presence of trade-offs between two or more conflicting objectives. Chapter III.1 introduces a multi-objective stochastic MILP model, which simultaneously accounts for the maximization of the expected total profit and minimization of the financial risk, for the strategic design and planning of the EGCM infrastructure under uncertainty in prices and operating costs. Chapter III.2 proposes a comprehensive infrastructure assessment model for CCS (CaimCCS) which integrates the major assessment methods of CCS such as TEA, EA, TRA under uncertainty in their input data. It is a grandiose attempt to include a large set of integrated multi-objective decisions within a single mathematical modeling framework considering uncertainties. The capability of the proposed model is tested by applying it to design and operate the future CCS infrastructure for treating CO2 emitted by the use of carbon-based fossil fuels in emission sources (e.g., power plants, steel plants, petrochemical plants, oil refinery) on the entire region of Korea in 2020. The result helps decision makers to establish an optimal strategy, which balances economy, environment and safety efficiency against stability in an uncertain future CCS infrastructure.