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Strategic Planning of Carbon Capture & Storage (CCS) Infrastructure Considering the Uncertainty in the Operating Cost and Carbon Tax
Abstract
A carbon capture and storage (CCS) plays a very important role to reduce dramatically in emission sources which are distributed throughout various areas. Numerous research works have been undertaken to analyze the techno-economic feasibility of planning the CCS infrastructure. However, uncertainties such as emissions, reduction costs, and carbon taxes may exist in various impact factors of the CCS infrastructure. However, few research works have adopted these uncertainties in designing the CCS infrastructure. In this study, a two-stage stochastic programming model is developed for planning the CCS infrastructure under uncertain operating costs and carbon taxes. It can help determine where and how much to capture, store or transport for the purpose of minimizing the total annual reduction cost in handling the uncertainties while meeting the mitigation target. The capability of the proposed model to provide correct decisions despite changing the operating costs and carbon taxes is tested by applying it to a real case study based on Korea. The results will help to determine planning of a CCS infrastructure under uncertain environments.
Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network.
It is difficult to clearly estimate CO2 emissions because CO2 is emitted from various sources according to changing environments. Any approach relating to CO2 disposal has to deal with the presence of such uncertainty in CO2 disposal demand. A two-stage stochastic programming model is developed for planning CO2 emissions disposal networks by taking into account this uncertainty. The proposed CO2 disposal network model allows us to determine where and how much captured CO2 can be held for storage, and where to sequester the given amount of CO2 among multiple potential candidates for the purpose of minimizing the total cost of handling in the face of uncertainty in CO2 sequestration demand. The proposed model is applied to a case study that examines the robustness of the model in terms of handling changing environments in the context of CO2 emissions and disposal targets in Korea. The results gained aid in determining policy to plan the budget for the disposal of CO2.
Numerous research works have been undertaken to plan carbon capture and storage (CCS) infrastructures for CO2 utilization and disposal. CO2 emissions are difficult to estimate precisely, because CO2 is emitted from various sources at varying rates. In this study, a two-stage stochastic programming model is developed for planning CCS infrastructure including CO2 utilization and disposal under stochastic CO2 emissions. The proposed model considers uncertainties in the variation of CO2 emissions. It can help determine where and how much CO2 to capture, store, transport, utilize, or sequester for the purpose of maximizing the total profit of handling uncertain CO2 emissions while meeting the CO2 mitigation target. The capability of the proposed model to provide correct decisions despite changing CO2 emissions is tested by applying it to an industrial complex on the eastern coast of Korea in 2020. The results will help to determine planning of, and budgeting for, development of a CCS infrastructure.
Much of the previous research on carbon capture and storage (CCS) has focused on individual technologies for disposing of CO2, such as capture, storage, sequestration, or transport. Moreover, recent research work considers utilization of CO2 as fuels, chemicals, or nutrients for bioreactors. To efficiently manage CO2 and the economic benefits achieved by this process, the CO2 transport and processing infrastructure supporting CCS will have to be constructed at a macro-scale. This paper introduces a scalable and comprehensive infrastructure model for CO2 utilization and disposal that generates an integrated, profit-maximizing CCS system. The proposed model determines where and how much CO2 to capture, store, transport, utilize or sequester to maximize total annual profit while meeting the CO2 mitigation target. The applicability of the proposed model is demonstrated using a case study for treating CO2 emitted by an industrial complex on the eastern coast of Korea in 2020. The results may be important in systematic planning of a CCS infrastructure and in assisting national and international policy makers to determine investment strategies for developing CCS infrastructures.
In a power-generation system, power plants as major CO2 sources may be widely separated, so they must be connected into a comprehensive network to manage both electricity and CO2 simultaneously and efficiently. In this study, a scalable infrastructure model is developed for planning electricity generation and CO2 mitigation (EGCM) strategies under the mandated reduction of GHG emission. The EGCM infrastructure model is applied to case studies of Korean energy and CO2 scenarios in 2020; these cases consider combinations of prices of carbon credit and total electricity demand fulfilled by combustion power plants. The results highlight the importance of systematic planning for a scalable infrastructure by examining the sensitivity of the EGCM infrastructure. The results will be useful both to help decision makers establish a power-generation plan, and to identify appropriate strategies to respond to climate change.Highlights► We model an infrastructure for planning electricity generation and CO2 mitigation. ► We examine changes in prices of carbon credit and electricity demands, respectively. ► Results propose that the electricity demand fulfilled by combustion plants be low. ► The policy managers can make an optimal decision considering energy and environment.
In this study, a two-stage inexact-stochastic programming (TISP) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a general optimization framework. The TISP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. The developed method is applied to a case study of energy systems and CO2 emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints.
This study provides insight into the feasibility of a CO2 trunkline from the Netherlands to the Utsira formation in the Norwegian part of the North Sea, which is a large geological storage reservoir for CO2. The feasibility is investigated in competition with CO2 storage in onshore and near-offshore sinks in the Netherlands. Least-cost modelling with a MARKAL model in combination with ArcGIS was used to assess the cost-effectiveness of the trunkline as part of a Dutch greenhouse gas emission reduction strategy for the Dutch electricity sector and CO2 intensive industry. The results show that under the condition that a CO2 permit price increases from €25 per tCO2 in 2010 to €60 per tCO2 in 2030, and remains at this level up to 2050, CO2 emissions in the Netherlands could reduce with 67% in 2050 compared to 1990, and investment in the Utsira trunkline may be cost-effective from 2020–2030 provided that Belgian and German CO2 is transported and stored via the Netherlands as well. In this case, by 2050 more than 2.1 GtCO2 would have been transported from the Netherlands to the Utsira formation. However, if the Utsira trunkline is not used for transportation of CO2 from Belgium and Germany, it may become cost-effective 10 years later, and less than 1.3 GtCO2 from the Netherlands would have been stored in the Utsira formation by 2050. On the short term, CO2 storage in Dutch fields appears more cost-effective than in the Utsira formation, but as yet there are major uncertainties related to the timing and effective exploitation of the Dutch offshore storage opportunities.
We sketch four possible pathways how carbon dioxide capture and storage (CCS) (r)evolution may occur in the Netherlands, after which the implications in terms of CO2 stored and avoided, costs and infrastructural requirements are quantified. CCS may play a significant role in decarbonising the Dutch energy and industrial sector, which currently emits nearly 100 Mt CO2/year. We found that 15 Mt CO2 could be avoided annually by 2020, provided some of the larger gas fields that become available the coming decade could be used for CO2 storage. Halfway this century, the mitigation potential of CCS in the power sector, industry and transport fuel production is estimated at maximally 80–110 Mt CO2/year, of which 60–80 Mt CO2/year may be avoided at costs between 15 and 40 €/t CO2, including transport and storage. Avoiding 30–60 Mt CO2/year by means of CCS is considered realistic given the storage potential represented by Dutch gas fields, although it requires planning to assure that domestic storage capacity could be used for CO2 storage. In an aggressive climate policy, avoiding another 50 Mt CO2/year may be possible provided that nearly all capture opportunities that occur are taken. Storing such large amounts of CO2 would only be possible if the Groningen gas field or large reservoirs in the British or Norwegian part of the North Sea will become available.
This paper presents linear models of the most common components in the value chain for capture and storage. The optimal investment planning of new gas power plants traditionally includes the cost of fuel versus sales of electricity and heat from the plant. If a new power plant also causes additional investments in gas infrastructure, these should be included in the optimization. With the increasing focus on global emissions, yet another aspect is introduced in the form of technology and infrastructure for capture, transport, and storage of . To be able to include all these aspects in the planning of new power plants, linear models for capture and storage are formulated consistent with current models for gas, electricity, and heat infrastructures. This paper presents models for the following infrastructure: source, combined cycle gas turbine producing electricity, heat and exhaust, capture plant, pipeline, liquefaction plant, storage, ship transport, injection pump, and demand/market.