Development Of An Inexact Carbon Capture And Storage Systems Optimization Model

In recent years, climate change has caused widespread concern around the world, the international community generally believes that the largest contribution to climate change is CO2emissions from the burning of fossil fuel, In order to reduce CO2emissions, it’s necessary to take effective measures to reduce CO2emissions produced by human activities, where carbon capture and storage (CCS) technology is an important element in a series of reduction measures. However, as a new abatement technology, CCS is still stay at the early phase of research and development and project demonstration. It has not completely appeared a commercial full-chain demonstration projects, which contains CO2capture, transport and storage. For effective strategic planning the development of CCS, researchers from all over the world proposed a variety of roadmaps. Nevertheless, the previous research efforts mostly emphasized on analyzing individual sector of CCS (e.g., capture sector), or just a simple coupling of all parts. These studies ignored the interactions among all sectors of CCS from the perspective of the whole system optimization, and neglected a full-chain system planning under the complex relationships of competition or coexistence of multiple technologies. Furthermore, CCS system is a complex system, which consists of three key subsystems: capture, transport and storage. There are a variety of technological options in each subsystem, which are not only further highly interrelated, but also impacted by a variety of social, economic, political, environmental, secure and technical factors. Such complexities lead to high uncertainty in long-term planning of CCS system.Therefore, the aim of this study is to develop an inexact carbon capture and storage systems optimization model for supporting the planning of technology commercialization in each sector and even in fall-chain CCS system and the analyzing the of various incentive policies and mechanisms from the perspective of source-sink matching under a CCS system optimization framework, hence to provide scientific bases for identifying strategic role of CCS and decision making on CCS in a national level. Toward this target and based on the previous research on CCS and uncertainty analysis, this study integrates interval-parameter programming and mixed-integer programming within a regional CCS system optimization model, leading to the development of an inexact carbon capture and storage system (ICCS) optimization model. The optimization model is then applied to a case study for long-term planning of a regional-scale CCS system. This case study demonstrated the applicability and effectiveness of the model in planning the development of CCS technologies, identifying financial incentive measures and addressing system uncertainty. The modeling results indicated that, to meet the national CO2reduction target, the CCS would be firstly implemented in industries with low capture cost (e.g. coal-chemical industry, natural-gas processing industry), followed by coal-fired power generation industry, which would be the main industry of the CCS application afterwards due to its emission characteristics. The results of scenario analysis further showed that CCS technology would be widely adopted with increase of domestic carbon price. This indicated that the key driving force of CCS in power industry would rely on the financial measures either from direct funding subsiderary or indirect financial policies.