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Multiperiod Stochastic Optimization Model for Carbon Capture and Storage Infrastructure under Uncertainty in CO2 Emissions, Product Prices, and Operating Costs
Abstract
A multiperiod stochastic programming model is developed for planning a carbon capture and storage (CCS) infrastructure including CO2 utilization and disposal in an uncertain environment and with a time-varying investment environment. An inexact two-stage stochastic programming approach is used to analyze the effect of possible uncertainties in product prices, operating costs, and CO2 emissions. The proposed model determines where and how much CO2 to capture, store, transport, utilize, or sequester for the purpose of maximizing the total profit of handling the uncertainty while meeting the CO2 mitigation target during each time period of a given planning interval. The capability of the proposed model to provide correct decisions despite a changing uncertain environment is tested by applying it to designing and operating the future CCS infrastructure on the eastern coast of Korea over a 20-year planning interval (2011–2030).
... In the literature, stochastic models are mainly applied to CCS supply chains [37][38][39][40]. Han and Lee [39] considered a CCS infrastructure with uncertainties on carbon dioxide sources. ...
... Han and Lee [39] considered a CCS infrastructure with uncertainties on carbon dioxide sources. In another work, the variability of operating and production costs is considered [38]. In [41], uncertainties are considered in the carbon dioxide price and tariff eligible per ton of carbon dioxide transported. ...
This paper develops a two-stage stochastic mixed integer linear programming model to optimize Carbon Capture, Utilization and Storage (CCUS) supply chains in Italy, Germany and the UK. Few works are present in the literature about this topic, thus this paper overcomes this limitation considering carbon supply chains producing different products. The objective of the numerical models is to minimize expected total costs, under the uncertainties of the production costs of carbon-dioxide-based compounds. Once carbon dioxide emissions that should be avoided are fixed, according to environmental protection requirements for each country, the optimal design of these supply chains is obtained finding the distribution of carbon dioxide captured between utilization and storage sections, the amount of different carbon-based products and the best connection between each element inside the system. The expected total costs for the CCUS supply chain of Italy, Germany and the UK are, respectively, 77.3, 98.0 and 1.05 billion€/year (1004, 613 and 164 €/ton CO2 captured). A comparison with the respective deterministic model, analyzed elsewhere, is considered through the evaluation of the Expected Value of Perfect Information (EVPI) and the Value of Stochastic Solution (VSS). The former is 1.29 billion€/year, 0.18 million€/year and 8.31 billion€/year, respectively, for the CCUS of Italy, the UK and Germany. VSS on the other hand is equal to 1.56 billion€/year, 0 €/year and 0.1 billion€/year, respectively, for the frameworks of Italy, the UK and Germany. The results show that the uncertain production cost in the stochastic model does not have a significant effect on the results; thus, in this case, there are few advantages in solving a stochastic model instead of the deterministic one.
... The methods for different types of the model of the whole process of CCUS are compared in Table 4. Considering variation of the emissions reduction target, a multiperiod model for planning CCS infrastructure is developed by maximising the average annual profit (TAP) as the single-objective function (Han et al., 2012b). On the basis of Han et al. (2012b), a multi-period stochastic optimisation model for CCS infrastructure is established under uncertainty in CO 2 emissions, product prices and operating costs (Han et al., 2012c). The planning task is formulated as a MILP problem that finds minimised cost and environmental impact, and then, a multi-objective optimisation approach is provided for CCS infrastructure (Lee et al., 2012). ...
... All above studies are optimisation models about infrastructure for decision-making. Han et al., (2012bHan et al., ( , 2012cHan et al., ( , 2013 and Lee et al., (2012) all consider the uncertainty. ...
Carbon Capture, Utilisation and Storage (CCUS) technology is an effective and economic method to reduce incredibly increasing greenhouse gas emissions. Mathematical modelling and evaluating are the key technologies for CCUS development. In this paper, we discuss the model and evaluation of CO2 capture from power plant, CO2 transport by pipeline, CO2-Enhanced Oil Recovery (EOR) and storage, respectively. Then, we investigate the whole process model and evaluation of CCUS based on the aforementioned sub-models. The purpose of this paper is to review the state-of-the-art of such research and development, and point out the unsolved issues and the future progression work.
... [1] SCO has emerged as a major research direction in the process systems engineering (PSE) community since the last decade; in the context of PSE, it is sometimes called enterprise-wide optimization if the emphasis is placed on the manufacturing stage. [2] In the PSE literature, SCO has been intensively studied for a variety of process industries, such as petroleum industry, [3][4][5] bioenergy industry, [6][7][8] pharmaceutical industry, [9][10] etc. Papageorgiou gave a comprehensive summary and discussion on the advances and opportunities of supply chain optimization for the process industries. [11] Nikolopoulou and Ierapetritou presented a review on the optimization of sustainable chemical processes and supply chains for balanced economic, environmental and social objectives. ...
... i ∈I, ω = 1,!,s , Here Eqs. (B.1-16) are obtained from Eqs. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) in the deterministic formulation (DSCO), through replacing variables for the material/product flow rates in scenario ω with affine functions of uncertainty realizations ξ ω . Specifically, ...
This paper is concerned with strategic optimization of a typical industrial chemical supply chain, which involves a material purchase and transportation network, several manufacturing plants with on-site material and product inventories, a product transportation network and several regional markets. In order to address large uncertainties in customer demands at the different regional markets, a novel robust scenario formulation, which has been developed by the authors recently, is tailored and applied for the strategic optimization. Case study results show that the robust scenario formulation works well for this real industrial supply chain system, and it outperforms the deterministic formulation and the classical scenario-based stochastic programming formulation by generating better expected economic performance and solutions that are guaranteed to be feasible for all uncertainty realizations. The robust scenario problem exhibits a decomposable structure that can be taken advantage of by Benders decomposition for efficient solution, so the application of Benders decomposition to the solution of the strategic optimization is also discussed. The case study results show that Benders decomposition can reduce the solution time by almost an order of magnitude when the number of scenarios in the problem is large. This article is protected by copyright. All rights reserved
... The study of d' Amore et al. (2019) also incorporates uncertain storage capacities and emission targets. Han and Lee (2012) consider uncertain amounts of CO 2 emissions, product prices, and operating costs in their multistage stochastic model. There are also multiple models that account for fluctuating carbon prices, either by using a multistage stochastic model (Elahi et al., 2017) or qualitative analysis (Sun and Chen, 2022). ...
The transition to a low-carbon economy necessitates effective carbon capture and storage (CCS) solutions, particularly for hard-to-abate sectors. Herein, pipeline networks are indispensable for cost-efficient transportation over long distances. However, there is deep uncertainty regarding which industrial sectors will participate in such systems. This poses a significant challenge due to substantial investments as well as the lengthy planning and development timelines required for pipeline projects, which are further constrained by limited upgrade options for already built infrastructure. The economies of scale inherent in pipeline construction exacerbate these challenges, leading to potential regret over earlier decisions. While numerous models were developed to optimize the initial layout of pipeline infrastructure based on known demand, a gap exists in addressing the incremental development of infrastructure in conjunction with deep uncertainty. Hence, this paper introduces a novel optimization model for pipeline infrastructure development, minimizing regret as its objective function and incorporating various upgrade options, such as looping and pressure increases. The model's effectiveness is also demonstrated by presenting a comprehensive case study of Germany's cement and lime industries. The developed approach quantitatively illustrates the trade-off between different options, which can help in deriving effective strategies for infrastructure development.
... Numerous studies have used stochastic models to explore CCS infrastructure design under parameter uncertainty [19][20][21][22][23][24]. However, these approaches either focus on highly simplified small-scale networks or do not incorporate pipeline routing optimization, which is critical for cost-effective deployment. ...
Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.
... where H is the time horizon [60]. The capital and operating costs for integrated hydrogen, methanation and natural gas are given by Equations (41) and (42), respectively, while the income is given by Equation (43). ...
In this paper, we propose an optimization model that considers two pathways for injecting renewable content into natural gas pipeline networks. The pathways include (1) power-to-hydrogen or PtH, where off-peak electricity is converted to hydrogen via electrolysis, and (2) power-to-methane, or PtM, where carbon dioxide from different source locations is converted into renewable methane (also known as synthetic natural gas, SNG). The above pathways result in green hydrogen and methane, which can be injected into an existing natural gas pipeline network. Based on these pathways, a multi-period network optimization model that integrates the design and operation of hydrogen from PtH and renewable methane is proposed. The multi-period model is a mixed-integer non-linear programming (MINLP) model that determines (1) the optimal concentration of hydrogen and carbon dioxide in the natural gas pipelines, (2) the optimal location of PtH and carbon dioxide units, while minimizing the overall system cost. We show, using a case study in Ontario, the optimal network structure for injecting renewable hydrogen and methane within an integrated natural gas network system provides a $12M cost reduction. The optimal concentration of hydrogen ranges from 0.2 vol % to a maximum limit of 15.1 vol % across the network, while reaching a 2.5 vol % at the distribution point. This is well below the maximum limit of 5 vol % specification. Furthermore, the optimizer realized a CO2 concentration ranging from 0.2 vol % to 0.7 vol %. This is well below the target of 1% specified in the model. The study is essential to understanding the practical implication of hydrogen penetration in natural gas systems in terms of constraints on hydrogen concentration and network system costs.
... The purpose of such optimization studies mainly includes the reduction in GHG emissions along with increased net profit. A multiperiod stochastic model for optimizing CCS infrastructure was formulated, 177 which was aimed at meeting CO 2 mitigation target while maximizing the profit. The stochastic parameters used rendered the model more realistic. ...
Carbon dioxide (CO2) emissions contribute considerably towards increasing greenhouse effect. Carbon capture and storage can reduce CO2 emissions to a great extent but lacks economic feasibility. The economic feasibility of CO2 capture could be boosted by utilizing the captured gas to produce valuable end products. CO2 is a highly stable molecule; therefore, special catalysts and elevated conditions of temperature and pressure are required for its conversion. This review presents the current status of CO2 utilization processes from various aspects, including thermodynamic, economic, and environmental impacts. The use of process systems engineering (PSE) tools and techniques in a broad spectrum, to improve the technical, economic, and environmental feasibility of these processes, is the major focus of this review. In this regard, a framework has also been presented showing the integration of various PSE techniques. All the related information in the form of tabulated data as well as qualitative and quantitative plots have been presented and critically analyzed.
... This model also used a discrete pipeline capacity cost function that requires more integer variables in the MILP formulation than our model. Multiple studies have produced multi-phased models that allow for variations in cost and capacity parameters, but do not allow for detailed pipeline routing [4][5][6] . ...
CO2 capture and storage (CCS) is a climate change mitigation strategy aimed at reducing the amount of CO2 vented into the atmosphere by capturing CO2 emissions from industrial sources, transporting the CO2 via a dedicated pipeline network, and injecting it into geologic reservoirs. Designing CCS infrastructure is a complex problem requiring concurrent optimization of source selection, reservoir selection, and pipeline routing decisions. Current CCS infrastructure design methods assume that project parameters including costs, capacities, and availability, remain constant throughout the project’s lifespan. In this research, we introduce a novel, multi-phased, CCS infrastructure design model that allows for analysis of more complex scenarios that allow for variations in project parameters across distinct phases. We demonstrate the efficacy of our approach with theoretical analysis and an evaluation using real CCS infrastructure data.
... Several studies optimised CCS SCs at region-to-country scale, e.g. Han and Lee (2012) and Lee et al. (2017) for the region of Pohang in South Korea, Elahi et al. (2014Elahi et al. ( , 2017 for the United Kingdom, Kalyanarengan Ravi et al. (2016) for the Netherlands, Middleton and Yaw (2018) for Alberta (Canada). Higher-level continent-wide analyses were proposed as well. ...
Carbon capture and storage technologies are key to remove carbon dioxide emissions from hard-to-decarbonise industries, such as steel, cement and refining sectors. A multi-echelon mixed integer linear programming model is developed for the optimal design of carbon capture and storage supply chains from industrial sources at a European level.
The model is based on exact coordinates and comprises all the stages of a carbon capture and storage chain in Europe, including multiple capture plants across industrial sources, CO2 transport through pipelines or ships to existing docks, onshore and/or offshore geological storage basins. The optimal infrastructure is optimised in economic terms by pursuing either country-wise or Europe-wide carbon reduction targets.
Removing 50% of industrial CO2 emissions in each country costs 60.5 €/t, which increases up to 81.4 €/t if onshore storage is forbidden. Variations of carbon reduction target are analysed. Although CO2 transport by pipeline dominates in terms of volumes, ships can have an important role for Southern Europe countries, if CO2 storage is restricted to offshore North Sea basins. The setting of a Europe-wide reduction target produces a slight decrease in costs in all analysed scenarios.
... Several studies in the past are trying to analyze a part or a whole of CCUS supply chain. Han and his colleague [26] calculated costs for an entire CCS system on the eastern coast of Korea under uncertainties in product prices, operating costs, and CO 2 emissions. In the next step, Han [27] improved his previous model to integrate multiobjectives including techno-economic, environmental, and technical risk evaluation. ...
In this study, the carbon capture, utilization, and sequestration (CCUS) supply chain network with real geographic locations of sources and sinks, and different CO 2 -based products for Germany is proposed here for the first time, because not yet investigated in the literature. The CCUS network is a large-scale comprehensive model which is used to meet the mandated target of CO 2 emission reduction at different levels with a maximum profit. The novel CCUS infrastructure includes various stationary sources, capture processes, transportation modes, and sequestration and utilization sites. The results suggest that it is possible to reduce current CO 2 emissions by 40–80% in Germany with the total annual costs ranging from 519.34 to 1372.03 billion euro while generating 681.55 to 1880.98 billion euro of revenue annually as a result of producing CO 2 -based chemical products including methanol, dimethyl ether, formic acid, acetic acid, urea, and polypropylene carbonate. Overall, the optimal CCUS systems achieve economic profits of 999.62–1568.17 euro per ton of CO 2 captured and utilized. The CCUS model may be critical in aiding decision-makers to ascertain investment strategies for designing CCUS infrastructures.
... Chance-constrained Table 1. Summary of representative works that use stochastic programming for PSE applications work application sources of uncertainty Gupta and Maranas (2003) supply chain planning demand Kim et al. (2011) biomass supply chain network supply, demands, prices, processing technologies Guillén-Gosálbez and Grossmann (2009) chemical supply chain life cycle inventory Gebreslassie et al. (2012) hydrocarbon biorefinery supply chains supply, demand Liu and Sahinidis (1996) process planning supply, demand Pistikopoulos and Ierapetritou (1995) process design supply, demand Acevedo and Pistikopoulos (1998) process synthesis supply, demand Goel and Grossmann (2004) offshore gas field developments gas reserves Zeballos et al. (2014) design and planning of supply chain supply, demand Levis and Papageorgiou (2004) capacity planning in pharmaceutical industry clinical trial outcomes Karuppiah and Grossmann (2008) synthesis of water networks contaminants concentration, removal efficiency Colvin and Maravelias (2008) clinical trial planning clinical trial outcomes Li et al. (2011a) natural gas production network design and operation quality of natural gas Sand and Engell (2004) real-time scheduling of batch plant processing time, yields, capacity, demand Liu et al. (2010) polygeneration energy systems design price, demand Paules IV and Floudas (1992) synthesis of heat-integrated distillation feed composition and flowrate Chu and You (2013) integrated scheduling and dynamic optimization process uncertainty, e.g., kinetic parameters Ye et al. (2014) production scheduling of steelmaking demand Zhang et al. (2016) scheduling of power-intensive processes electricity price Legg et al. (2012) gas detector placement leak locations, weather conditions Han and Lee (2012) carbon capture and storage infrastructure design CO2 emissions, product prices, operating costs Zavala (2014) control of natural gas networks demand shale gas infrastructure planning production This is a provisional file, not the final typeset article programming can be seen as solving a stochastic program with some probabilistic constraints, which specify that some constraints with uncertain parameters are satisfied with a given level of probability. For example, a chance constraint can specify that a budget/cost that should not pass a certain threshold. ...
Uncertainties are widespread in the optimization of process systems, such as uncertainties in process technologies, prices, and customer demands. In this paper, we review the basic concepts and recent advances of a risk-neutral mathematical framework called “stochastic programming” and its applications in solving process systems engineering problems under uncertainty. This review intends to provide both a tutorial for beginners without prior experience and a high-level overview of the current state-of-the-art developments for experts in process systems engineering and stochastic programming. The mathematical formulations and algorithms for two-stage and multistage stochastic programming are reviewed with illustrative examples from process industries. The differences between stochastic programming under exogenous uncertainty and endogenous uncertainties are discussed. The concepts and several data-driven methods for generating scenario trees are also reviewed.
... In analogy with other complex frameworks (e.g., energy systems), mathematical programming techniques and in particular mixed integer linear programming (MILP) have been recently exploited for optimising CCS SCs and foster their effective implementation at different scales (Tapia et al., 2018). CCS optimisation was first proposed for Ohio (United States) by Turk et al. (1987), followed by other region-like sized modelling systems, for instance that by Han and Lee (2012) located in Pohang (South Korea), by Middleton et al. (2012) in Texas (United States), by Middleton and Yaw (2018) in Alberta (Canada), or contemplating the installation and operation of the network in a hypothetical area (e.g., Zhang et al., 2018). Higher level CCS SCs were optimised at national scale as well, as in the contributions by Klokk Schreiner et al. (2010) in Norway, Elahi et al. (2014; and Nie et al. (2017) in the United Kingdom, Kalyanarengan Ravi et al. (2016) in the Netherlands, A grali et al. (2018) in Turkey, Leonzio et al. (2019) in Germany, and Zhang et al. (2020) in Northeast China. ...
Power, steel, cement and refining sectors are currently responsible for the largest shares of carbon dioxide emissions from stationary sources. Carbon capture and storage is envisioned as a key player for decarbonising the power and industry sectors. To achieve a significant penetration of carbon capture and storage technologies, supply chain optimisation has emerged as a crucial research task for designing such complex systems.
A Europe-wide carbon capture and storage supply chain is here optimised via a mixed integer linear programming framework. The most significant carbon dioxide emitters (242 power plants, 25 steel mills, 111 cement plants and 59 refineries) are identified on exact geographic coordinates and included as candidates for capture. Capture plants are thoroughly represented in techno-economic terms, considering scale effects and different technological options. Transport and sequestration stages are implemented for either onshore or offshore operation.
Different case studies are taken into account to assess carbon capture and storage policies and results determine optimal configurations in terms of costs, scale effects, technology options and network complexity. The minimum CO2 avoidance cost is 52 €/t, which increases by 9% if power plants are excluded from carbon sources. If offshore storage is preferred to onshore, cost raises by about 40%.
... Several contributions analyzed, mostly through mixed integer linear programming (MILP) techniques, the design and optimization of CCS systems for different geographic contexts and scales ( Table 1). Han and Lee (2012) optimized a CCS SC for North Korea through a MILP formulation under uncertainty in market prices while, again for North Korea, a subsequent contribution considered preference on risk as a measure of data uncertainty (Lee et al., 2017). Middleton et al. (2012) proposed a MILP model for CCS located in Texas that also took into account uncertainty in storage physics. ...
Carbon capture and storage represents a key technology for reducing the anthropogenic emissions of greenhouse gases. In addition to this, carbon utilization has often been considered as a viable option for increasing the environmental benefits, while decreasing costs of the mere capture and storage system. This contribution proposes an optimization framework for the design of carbon capture, transport, utilization, and storage supply chains in the European context. Based on literature data, technologies converting CO2 into methanol and polyether carbonate polyols were selected as the most promising and incorporated into the optimization framework. The goal is to reduce 50% of European emissions from large stationary sources by 2030. Results highlight that, under our assumptions, the significance of carbon utilization in terms of a reduction of the environmental impact is likely to be a minor one: considering the current state of technologies only about 2.4% of the overall CO2 emitted from large stationary sources can be removed by chemical utilization. However, significant benefits can be obtained in terms of overall cost reduction thanks to revenues deriving from the chemicals being produced.
... Despite the broad variety of investigated parameters, mainly related to economics Lee, 2013, 2012;Jin et al., 2017;Noureldin et al., 2017;Wu et al., 2015 ), storage injectivity ( Aminu et al., 2017;Jeong et al., 2018;Nie et al., 2017;Noureldin et al., 2017;Petvipusit et al., 2014;Zhang et al., 2018 ), storage leakage ( Alcalde et al., 2018;Aminu et al., 2017;García and Torvanger, 2019 ) and policy ( Jin et al., 2017 ), and the abundancy of employed methodologies, e.g. multiple scenario realisation ( Han and Lee, 2012;Nie et al., 2017 ), or inexact optimisation ( Han and Lee, 2013;Wu et al., 2015 ), none of the previous studies have focused on the quantification of risk that may emerge as a consequence of uncertainty in the actual geological volume that is available for CO 2 sequestration in the large-scale European context. For instance, Bachu (2003) proposed a set of 15 criteria to assess basins in terms of suitability for storage in the small scale context of Alberta (Canada), but the comprehensive effects on the design of a CCS infrastructure is not addressed. ...
Carbon capture and storage is widely recognised as a promising technology for decarbonising the energy and industrial sector. An integrated assessment of technological options is required for effective deployment of large-scale infrastructures between the nodes of production and sequestration of CO2. Additionally, design challenges due to uncertainties in the effective storage availability of sequestration basins must be tackled for the optimal planning of long-lived infrastructure. The objective of this study is to quantify the financial risks arising from geological uncertainties in European supply chain networks, whilst also providing a tool for minimising storage risk exposure. For this purpose, a methodological approach utilising mixed integer linear optimisation is developed and subsequent analysis demonstrates that risks arising from geological volumes are negligible compared to the overall network costs (always <1% of total cost) although they may be significant locally. The model shows that a slight increase in transport (+11%) and sequestration (+5%) costs is required to obtain a resilient supply chain, but the overall investment is substantially unchanged (max. +0.2%) with respect to a risk-neutral network. It is shown that risks in storage capacities can be minimised via careful design of the network, through distributing the investment for storage across Europe, and incorporating operational flexibility.
... The capability of their inexact model is tested by an industrial region on the eastern coast of Korea in 2020. Then they extend this method by developing a refined set of scenarios and considering other uncertainties such as operating cost and product cost [36]. In their study, uncertainties are assumed to follow a set of scenarios associated with given probabilities of occurrence. ...
... In addition, LiÀ CO 2 /O 2 batteries have the potential to be a revolutionary solution for carbon capture and electrical energy generation among various methodologies, including chemical and physical methods, currently under development. [10][11][12] However, the discharge product Li 2 CO 3 is difficult to decompose and may result in a large overpotential. Surprisingly, a high dielectric medium can realize the reversible reaction of Li 2 CO 3 , which provides a mechanism for the development of a reusable Li-air battery. ...
As a promising energy storage technology, Li−CO2/O2 battery with ultrahigh discharge capacities have received much attention, reaching capacities three times that of Li−O2 batteries. Herein, using an excellent catalyst, NiCo2O4 designed as a 3D dandelion‐like hollow nanostructure, a Li−CO2/O2 battery is systematically investigated to understand how the reaction mechanisms are affected by CO2. With CO2 stabilization, the batteries could achieve a specific discharge capacity as high as 22000 mAh/g and a long‐term cycling performance of up to 140 cycles without apparent deterioration. In addition, the intrinsic mechanism of the current density influence is explored based on the Li2CO3 morphology evolution. Superoxide anion radical species (O2.− ) were identified to be rapidly consumed by CO2, which dramatically enhances the stability of Li−O2 batteries. The results indicate that the NiCo2O4 nanocatalyst can efficiently inhibit Li2CO3 aggregation and realize the maximum utilization of active sites. The results confirm that the 3D dandelion‐like NiCo2O4 catalyst can be a potential cathode for Li−CO2/O2 batteries.
... Stochastic optimization model could be the effective way to solve this problem. Stochastic optimization is a method widely used in the field of energy planning [15,16], energy system planning [17][18][19][20][21][22][23], CCS infrastructure planning [24,25], and Combined Cooling, Heating, and Power (CCHP) generation systems [26]. In terms of studies on stochastic optimization in the power sector, there are a few studies [27][28][29][30]. ...
In order to solve problems caused by climate change, countries around the world should work together to reduce GHG (greenhouse gas) emissions, especially CO2 emissions. Power demand takes up the largest proportion of final energy demand in China, so the key to achieve its goal of energy-saving and emission reduction is to reduce the carbon emissions in the power sector. Taking Shenzhen as an example, this paper proposed a stochastic optimization model incorporating power demand uncertainty to plan the carbon mitigation path of power sector between 2015 and 2030. The results show that, in order to achieve the optimal path in Shenzhen’s power sector, the carbon mitigation technologies of existing coal and gas-fired power plants will be 100% implemented. Two-thirds and remaining one-third of coal-fired power plant capacities are going to be decommissioned in 2023 and 2028, respectively. Gas-fired power, distributed photovoltaic power, waste-to-energy power and CCHP (Combined Cooling, Heating, and Power) are going to expand their capacities gradually.
... Recently, a few papers on CO 2 SC optimisation appeared, mostly investigating CCS networks in terms of economic optimisation (e.g., Bakken and von Streng Velken, 2008;Elahi et al., 2014;Kalyanarengan Ravi et al., 2017;Middleton et al., 2012;Strachan et al., 2011), in some cases including CO 2 utilisation for process integration too (e.g., Han and Lee, 2012;Han and Lee, 2014;Hasan et al., 2015;Klokk et al., 2010;Wu et al., 2015), or assessing some environmental-or risk-related aspects (Han and Lee, 2013). Most contributions focussed on a specific region or country. ...
Diminishing the anthropogenic generation of greenhouse gases is one of the key challenges of the twenty-first century. Considering the current state of affairs, it is barely impossible to reduce emissions without relying on CO2 capture and sequestration technologies. In a situation where a large-scale infrastructure is yet to be developed, mathematical programming techniques can provide valuable tools to decision makers for optimising their choices. Here, a mixed integer linear programming framework for the strategic design and planning of a large European supply chain for carbon geological storage is presented. The European territory is discretised so as to allow for a spatially explicit definition of large emission clusters. As regards CO2 capture, post-combustion, oxy-fuel combustion and pre-combustion are considered as possible technological options, whereas both pipelines (inshore and offshore) and ships are taken into account as possible transport means. The overall network is economically optimised over a 20 years’ time horizon to provide the geographic location and scale of capture and sequestration sites as well as the most convenient transport means and routes. Different scenarios (capturing up to 70% of European CO2 emissions from large stationary sources) are analysed and commented on. Results demonstrate the good European potential for carbon sequestration and give some indications on the total cost for CO2 capture, transport and sequestration. Capture costs are found to be the major contribution to total cost, while transport and sequestration costs are never higher than 10% of the investment required to set in motion and operate the whole network. The overall costs for a European carbon capture, transport and storage supply chain were estimated in the range of 27-38 €/ton of CO2.
... Han and Lee [12] used a scalable and stochastic infrastructure model to financially optimize carbon sequestration, utilization, storage, and transportation with respect to a specific climate goal for the eastern part of South Korea. [15,16]. Recently, Lee et al. [17] introduced a CCS network-optimization tool that incorporates both economic and environment values, while also taking into account decisionmaking risk. ...
Synthetic hydrocarbons can be produced sustainably with power-to-gas processes, resulting in a net reduction of greenhouse gas emissions due to the substitution of conventional natural gas and other fossil fuels with carbon-neutral alternatives. Acquisition of the for the synthetic fuel production can be implemented in multiple ways. This work introduces a node-based model to assess different implementation strategies of utilization systems, taking into account temporal effects, regional variation, and economies of scale for capture. Intermediate storage volumes, capture costs, transport quantities, and other relevant infrastructural aspects of the CCU system can be estimated with the model. Finland is used as a case study, focusing specifically on the national and regional scale. capture costs are significant, being nearly four times larger than the cost of storage in the baseline scenario (354 M€, 85 M€). sources with smaller annual emissions increases capture costs by 14% compared to baseline. This increase in cost is comparable to the cost of transporting over a quarter of all captured to off-site processing (varying distance, 100–400 km). Seasonal storage of is found to be beneficial for the cost-efficient production of synthetic fuels, owing to the temporal disparity between emissions and utilization, as well as the overall cost structure of the components. Five key decision categories are proposed for a carbon utilization system: scale, type, units, location, and technological decisions. These may be applied to describe any carbon utilization system, helping to form a more comprehensive picture of a future energy system, where carbon is widely used as a raw material.
... Many uncertainty analysis techniques are applied extensively in various fields Farquhar 1985, 1986;Huang, 1998;Cohen, Shamir, and Sinai 2003;Aliab, Verlindena, and Oudheusden 2008;Rong and Lahdelma 2008;Xu et al. , 2010Xu, Huang, and Xu 2012;Qin, Huang, and Liu 2010;Fan, Huang, and Li 2012;Kundu, Kar, and Maiti 2014;Shao, Xu, and Huang 2014;Liu, Wen, and Xu 2015). With respect to CCUS management systems, one kind of common uncertainty is randomness (Koo, Han, and Yoon 2011;Han and Lee 2012;He et al. 2014) and another is fuzziness (Dai et al. 2014;Zhang et al. 2014aZhang et al. , 2014b. Interval numbers are also used to describe parameters varying in a small range, and are easy to integrate with other optimization methods (Dai et al. 2014;Zhang et al. 2014b). ...
Effective application of carbon capture, utilization and storage (CCUS) systems could help to alleviate the influence of climate change by reducing carbon dioxide (CO2) emissions. The research objective of this study is to develop an equilibrium chance-constrained programming model with bi-random variables (ECCP model) for supporting the CCUS management system under random circumstances. The major advantage of the ECCP model is that it tackles random variables as bi-random variables with a normal distribution, where the mean values follow a normal distribution. This could avoid irrational assumptions and oversimplifications in the process of parameter design and enrich the theory of stochastic optimization. The ECCP model is solved by an equilibrium change-constrained programming algorithm, which provides convenience for decision makers to rank the solution set using the natural order of real numbers. The ECCP model is applied to a CCUS management problem, and the solutions could be useful in helping managers to design and generate rational CO2-allocation patterns under complexities and uncertainties.
... Perez et al. [15] considered CO 2 transport in an absorbed phase by train and performed its economic analysis. Few other studies [16][17][18] considered tank trailers and trains as alternate transportation methods of CO 2 , but eventually concluded that they are economically infeasible, compared to pipeline or ship transport. This review paper is motivated by the fact that when CCS is to be deployed in full scale, very large quantities of CO 2 will have to be transported from the source to the storage sites. ...
With recent advancements in the carbon capture and storage (CCS), much interest has been developed in the CO2 transport options. In this paper, various aspects associated with the performance and safety of CO2 transportation are reviewed and discussed. The operational range for pipeline and ship transportation of CO2 is quite different, with both methods offering some cons and pros. CO2 stream composition has a great impact on the energy efficiency of the transport chain. In order to facilitate CO2 transportation and reduce its cost, regional and global sharing of transport network must be encouraged. Finally, an up-to-date status on the current and future CO2 transport projects has been presented.
... The scenario formulation retains the flexibility of choosing different second-stage decisions according to different realizations of uncertainty, and it often achieves a good estimation of the expected performance and returns reasonable solutions. The scenario formulation is widely adopted in the SCO research, and recent work includes applications in oil supply chains 9 , chemical supply chains 16 , biorefinery supply chains 17 , carbon capture and storage networks 18 , etc. Again, readers can refer to several recent survey papers for more relevant work in the PSE literature. [11][12][13] However, the scenario formulation cannot guarantee the feasibility of the solution (for the problem before the scenario approximation) in general, because not all the uncertainty realizations are examined. ...
Strategic supply chain optimization (SCO) problems are often modeled as two-stage optimization problems, in which the first-stage variables represent decisions on the development of the supply chain and the second-stage variables represent decisions on the operations of the supply chain. When uncertainty is explicitly considered, the problem becomes an intractable infinite-dimensional optimization problem, which is usually solved approximately using a scenario or a robust approach. This article proposes a novel synergy of the scenario and robust approaches for strategic SCO under uncertainty. Two formulations are developed, namely, naïve robust scenario formulation and affinely adjustable robust scenario formulation. It is shown that both formulations can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded by the infinity norm and the uncertain equality constraints can be reformulated into deterministic constraints without any assumption about the uncertainty region. Case studies of a classical farm planning problem and an energy and bioproduct SCO problem demonstrate the advantages of the proposed formulations over the classical scenario formulation. The proposed formulations not only can generate solutions with guaranteed feasibility or indicate infeasibility of a problem, but also can achieve optimal expected economic performance with smaller numbers of scenarios.
... Oil and gas fields and un-minable coal and saline formations have been suggested for geological storage. In geosequestration, various physical (e.g., highly impermeable cap rock) and geochemical trapping mechanisms prevent CO 2 from escaping to the surface (Herzog, 2001;Seto et al., 2007;Wood et al., 2008;Cinar et al., 2006;Singleton et al., 2009;Shukla et al., 2010;Oltra et al., 2010;Malone et al., 2010;Gough et al., 2010;Terwel et al., 2011;Zatsepina and Pooladi-Darvish, 2012;Han and Lee, 2012;De Silva et al., 2012). ...
Purpose
The Carbon Capture, Utilization and Storage (CCUS) network integrates pipelines, offshore shipping and trucks to transport captured CO2 to utilization facilities or storage sites like depleted oil fields and saline aquifers. However, it faces risks such as natural disasters and storage site and/or utilization facility disruptions, leading to uncertainty. Enhancing resilience in CCUS network design is essential to address these risks. This paper aims to develop a resilient CCUS supply chain network (SCN) that minimizes expected total cost under disruption risks while ensuring the required CO2 emission reduction target is met.
Design/methodology/approach
This paper proposes a novel chance-constrained programming approach with multiple-resilience strategies for optimal designing the resilient CCUS SCN problem under storage site and/or utilization facility disruptions, where the storage site and/or utilization facility capacity loss and the storage site and/or utilization facility fortification cost are assumed as uncertain parameters. The proposed uncertain model is transformed into a tractable deterministic equivalent model.
Findings
A case study from Guangdong Province, China, verifies the effectiveness of the proposed model. The sensitivity analysis examines the effects of the expected values of uncertain parameters on the total SCN cost. The results show that for capacity losses under 50%, the reactive transshipment strategy is more economical, particularly as losses decrease. Over 50%, proactive fortifications are more cost-effective, especially with greater losses.
Originality/value
This paper is the first to introduce two resilience strategies (fortifying storage sites and/or utilization infrastructure and lateral transshipment via offshore vessels) to bolster the CCUS network’s capacity to endure unforeseen disruptions. The two strategies are integrated into the proposed chance-constrained model. Our novel approach could help companies develop effective, sustainable and reliable CCUS networks capable of withstanding unforeseen risks.
The global anthropogenic generation of greenhouse gasses experienced an exponential increase compared to pre-industrial levels and, among these, CO2 is the most abundant, with an emission that rose globally from 2 Gt/year in 1850 to over 35 Gt/year in 2010. Carbon capture and storage has been highlighted among the most promising options to decarbonise the energy sector, especially considering the European context which heavily relies on fossil fuels. When dealing with the strategic design and planning of an international carbon capture and storage infrastructure, the necessity of taking into account the differential behaviour among the European countries in terms of public procurement and assignation delays emerges as a key requirement for attaining an effective implementation of the network. This contribution proposes a mixed integer linear programming modelling framework for the economic optimisation of a multinational European carbon capture and storage supply chain, including the effects of countrywide delays in public procurement. Assignation lags are implemented as an additional cost for the installation of the network. Results show that only minor modifications in the supply chain design should be taken into account with respect to an equivalent non-delayed scenario, with a consequent just moderate increase in transport costs (+ 3 %). Moreover, it is shown that capture and sequestration stages are barely not affected by the introduction of assignation lags among countries.
The mathematical model for carbon capture, utilization and storage supply chains is developed for Italian regions. This model is used to design supply chains minimizing total costs and reducing significantly carbon dioxide emissions. Carbon dioxide can be stored or utilized to produce methane through a power to gas system, where hydrogen is obtained by the electrolysis of water using renewable power while methane is fed to the gas grid. The Mixed Integer Linear Program model is applied to design carbon systems developed for ten regions with major carbon dioxide emissions, while different saline aquifers are proposed as storage sites. Results show that the Adriatic sea is the most appropriate offshore storage site in the supply chain. This leads to a lower net methane production cost and to the lowest level of economic incentives as compared to other cases. The total costs of this supply chain are 7.34·10⁴ million €/year (953 €/tonCO2 captured), and 16.1 Mton/year of methane are produced. The supply chain does not result economically favorable without substantial financial incentives (80 €/tonCO2 for carbon tax and 260 €/MWh economic incentive for methane production). Comparing supply chains that include simultaneous utilization and storage of carbon dioxide with a carbon capture and utilization supply chain (without storage) shows that the latter is economically less favorable. In addition to mitigation of carbon environmental impact for the whole Country, the large scale supply chain proposed here meets 35% of Italy's methane demand as a whole, a significant contribution to a global economic perspective, widely discussed nowadays, which should include self-sustainment elements at regional level.
Carbon Capture and Storage (CCS) is an essential technology for CO2 emissions reductions, which will allow us to continue consuming fossil fuels in the short to medium term. In this work, we developed a multiscale modeling and optimization approach that links detailed models of the capture plant, compression train and pipelines with the CO2 supply-chain network model. This was used to find the cost-optimal CO2 network considering a case-study of meeting a national reduction target in the United Arab Emirates that supplies CO2 for EOR activities. The main decision variables were the optimal location and operating conditions of each CO2 capture and compression plant in addition to the topology and sizing of the pipelines while considering the whole-system behaviour. A key result of our study was that the cost-optimal degree of capture should be included as a degree of freedom in the design of CO2 networks and it is a function of several site-specific factors, including exhaust gas characteristics, proximity to transportation networks and adequate geological storage capacity. This conclusion serves to underscore the need to comprehend the science governing the physical behaviour at different scales and the importance of a whole-system analysis of potential CO2 networks.
Reverse water gas shift on the basis of chemical looping technology provides a viable method for efficiently converting CO2 to CO for hydrocarbons at moderate temperature (<750 oC). However, the commonly available oxygen carrier materials are insufficiently active because of the degrading effect at relatively low temperatures. In this paper, we present several Co, Mn co-doped ferrites in search of oxygen carrier materials as highly active redox materials for mid-temperature chemical looping CO2 splitting process. The results show that up to ~142.3 μmol·g1·min-1 of CO production rate and ~8.8 mmol·g-1 of CO yield are achieved by the Mn0.2Co0.8Fe2O4 at 650 °C. The production rate and yield of CO of the co-doped ferrites during the CO2 splitting process is comparable to the state-of-the-art perovskites which commonly contain rare-metal elements.
In the 2016, CO2 emissions in the world was about 32.3 Gt with the combustion of fossil fuel being the highest contributor. A target for the reduction of CO2 emissions of 60% was set in the Paris Agreement. Thereafter, this topic has acquired much importance in the world, especially in recent years. In this context, carbon capture utilization and its storage supply chain is highly considered as a strategic solution, that can solve the problems related to CO2 emissions. In this work, an overview about carbon capture utilization and its storage supply chain is developed. Due to their important environmental role, mathematical models are required for design and optimization. Hierarchical or simultaneous methodology can be used, and one procedure is suggested for minimizing the total cost of supply chain. Following this, equations related to capture and compression costs, transportation costs, utilization and storage costs are revised. This work, in addition, reviews systems for CO2 capture and compression, and options for CO2 utilization and storage. Many work are present in literature regarding this technology, however more studies should be developed on dynamic state considering uncertainties.
Carbon Capture and Storage technique has been recognized as the most effective method of reducing the increasing concentration of carbon dioxide in the atmosphere with successful applications reported by different countries. This chapter gives a deeper insight into the major components of the carbon capture and storage technology as a major mitigation approach implemented in deep geological formations worldwide. The mechanisms, challenges, and issues understood so far linked to this technology are also presented and discussed in detail. It appears that if a carbon capture and storage project can be safely implemented considering all of the precautions mentioned in this chapter, a great step can be taken towards a better future for the next generation.
The installation of infrastructures for carbon capture, transport, and storage to tackle the problem of CO2 emissions from European power plants and carbon intensive industries, is of strategic importance to reach future greenhouse gases reduction targets. However, the public reaction to the deployment of these technologies is still uncertain, and opposition may result in either cancellations or delays. This article provides quantitative insights into how social acceptance affects the design of a European CO2 infrastructure. A multi-objective mixed integer linear programming model is developed to optimise the design the entire supply chain, by simultaneously addressing the minimisation of the costs to install and operate the infrastructure and the maximisation of its community acceptance. The goal is to provide optimal supply chains in terms of costs, whilst considering the social behaviour of inhabitants towards the installation and operation of either CO2 pipelines or injection wells. Results demonstrate how the methodology may be exploited to assess the response of local communities and identify design strategies aiming at a trade-off between economic objectives and social acceptance. Although the maximisation of social acceptance leads to a +34% increase in costs with respect to the economic optimum, it is shown that an intermediate solution between the two objectives (i.e., economics against acceptance) entails a just slight increase of +8% with respect to the cost of the best economic configuration.
A mathematical model for the optimal design of a supply chain for carbon capture, utilization and storage is developed. Carbon dioxide may be stored and/or utilized to produce methanol via methane dry reforming. The Mixed Integer Linear Program model is developed for ten major carbon dioxide emission sources in Germany. Three different cases are considered, according to hydrogen production route required to correct the syngas composition: hydrogen by external reforming, hydrogen by external water electrolysis, hydrogen by internal steam reforming. Results show that absorption is the preferred capture solutions at high flue gas flow rate. Results also show that the best option is providing hydrogen by water electrolysis, because a higher amount of carbon dioxide is used to produce the same amount of methanol with lower environmental impact. The proposed network produces 203 Mton/year of methanol, then Germany would be able to satisfy the world methanol demand in the next years. Carbon tax and economic incentives are required to reduce the methanol production cost to 340 €/ton: only in this case the process is economically feasible.
Carbon Integration is a novel concept that targets the recovery and allocation of industrially emitted CO2 streams into CO2-using sinks, with the goal of attaining a CO2 allocation strategy that meets a desired carbon dioxide emission reduction target, and an ultimate aim of minimizing the cost of the network, while maximizing any revenue attained. Enhanced Oil Recovery is considered one of the most attractive CO2 sink options. CO2 streams that are delivered and injected into EOR sites are great revenue sources for CO2 supplying entities. Since oil pricing heavily affects the revenue generated via CO2 streams injected into EOR sites, this paper studies the effect of oil price fluctuations onto the design of carbon integration networks. Hence, oil pricing has been selected as the main uncertainty parameter, and has been fed into a Linearized Multi-Period Carbon Integration model using stochastic data. Since oil prices vary periodically, this model has been formulated over several time periods, in which the oil pricing parameters are allowed to change over time. The proposed model has been optimized using two different approaches: (1) the Binomial Lattice approach, which primarily utilizes average uncertainties as expected values, and (2) the Multi-Scenario approach.
The issues of reducing CO2 emissions, sustainably utilizing natural mineral resources, and dealing with industrial waste offer challenges for sustainable development in energy and the environment. We propose an efficient methodology via the co-reaction of K-feldspar and phosphogypsum for the extraction of soluble potassium salts and recovery of SO2 with reduced CO2 emission and energy consumption. The results of characterization and reactivity evaluation indicated that the partial melting of K-feldspar and phosphogypsum in the high-temperature co-reaction significantly facilitated the reduction of phosphogypsum to SO2 and the exchange of K+ (K-feldspar) with Ca2 + (CaSO4 in phosphogypsum). The reaction parameters were systematically investigated with the highest sulfur recovery ratio of ~ 60% and K extraction ratio of ~ 87.7%. This novel methodology possesses an energy consumption reduction of ~ 28% and CO2 emissions reduction of ~ 55% comparing with the present typical commercial technologies for utilization of K-feldspar and the treatment of phosphogypsum.
We develop an economic process design for separation of CO2 from the off-gas in ISMPs (iron and steel making plants). Based on the characteristics of the off-gas from which CO2 must be separated, we design two process configurations: PSA (pressure-swing adsorption) and MEA (monoethanolamine)-based chemical absorption. We also develop a simulation model of each process, and perform an economic evaluation of the configurations. Our technical performance analyses show that the CO2 recovery and purity are >90% in the both processes and that highly-concentrated combustible gas (CO and H2) can be obtained as a byproduct. Our economic performance analyses show that the designed processes lead to cost-effective and competitive options ($62/t CO2 separated), compared to the CO2 separation processes used in power plants. Using the combustible gas as a fuel for the boiler of power cycle greatly reduces the cost of CO2 separation in ISMPs.
In this study, a comprehensive infrastructure assessment model for carbon capture and storage (CiamCCS) is developed for (i) planning a carbon capture and storage (CCS) infrastructure that includes CO2 capture, utilization, sequestration and transportation technologies, and for (ii) integrating the major CCS assessment methods, i.e., techno-economic assessment (TEA), environmental assessment (EA), and technical risk assessment (TRA). The model also applies an inexact two-stage stochastic programming approach to consider the effect of every possible uncertainty in input data, including economic profit (i.e., CO2 emission inventories, product prices, operating costs), environmental impact (i.e., environment emission inventories) and technical loss (i.e., technical accident inventories). The proposed model determines where and how much CO2 to capture, transport, sequester, and utilize to achieve an acceptable compromise between profit and the combination of environmental impact and technical loss. To implement this concept, fuzzy multiple objective programming was used to attain a compromise solution among all objectives of the CiamCCS. The capability of CiamCSS is tested by applying it to design and operate a future CCS infrastructure for treating CO2 emitted by burning carbon-based fossil fuels in power plants throughout Korea in 2020. The result helps decision makers to establish an optimal strategy that balances economy, environment, and safety efficiency against stability in an uncertain future CCS infrastructure.
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.
Most industries consume a large amount of energy to produce goods and to reduce the resultant CO2 emissions. We develop a systematic process integration framework for the optimal design and techno-economic performance analysis of energy supply and CO2 mitigation strategies. We first generate an Integrated Energy Supply and CO2 Mitigation Network (IESCMN), which includes three processes, each of which consists of a large number of technologies. This model allows identification of energy requirements (sinks) and unused energy sources of the technologies. Developing an optimization model for the IESCMN allows identification of a promising strategy to minimize energy cost, because sources and sinks can be connected to each other to transfer unused energy. Our techno-economic analysis results show that the IESCMN results in a considerable decrease in energy cost, compared to the individual operation of each technology.
A large number of research works were undertaken for the planning of sustainable electricity generation and CO2 mitigation (EGCM) infrastructure design under uncertainty. The typical methodologies assessed the performance of the problem under the variability of the uncertain parameters by optimizing the expected value of the objective function. This approach can have large probabilities of the value optimized in unfavorable scenarios. In this paper, we present a mathematical programming model in planning sustainable electricity generation and CO2 mitigation (EGCM) infrastructure design, including financial risk management under uncertainty. The proposed model allows us to determine available technologies to produce electricity and treat CO2 on the purpose of maximizing the expected total profit and minimizing the financial risk of handling uncertain environments (i.e. CO2 mitigation operating costs, carbon credit prices and electricity prices, etc.), while fulfilling electricity demands and CO2 mitigation standards. The multi-objective optimization problem was solved by using the weighted-sum method that imposes a penalty for risk to the objective function. The capability of the proposed modeling framework is illustrated and applied to a real case study based on Korea, for which valuable insights are obtained.
In view of the great contribution of coal-fired units to CO2 emissions, the coupled coal and power system with consideration of CO2 mitigation is a typical sub-system of the highly emitting Chinese energy system for low-carbon studies. In this study, an inexact mix-integer two-stage programming (IMITSP) model for the management of low-carbon energy systems was developed based on the integration of multiple inexact programming techniques. Uncertainties and complexities related to the carbon mitigation issues in the coupled coal and power system can be effectively reflected and dealt with in this model. An optimal CO2 mitigation strategy associated with stochastic power-generation demand under specific CO2 mitigation targets could be obtained. Dynamic analysis of capacity expansion, facility improvement, coal selection, as well as coal blending within a multi-period and multi-option context could be facilitated. The developed IMITSP model was applied to a semi-hypothetical case of long-term coupled management of coal and power within a low-carbon energy system in north China. The generated decision alternatives could help decision makers identify desired strategies related to coal production and allocation, CO2 emission mitigation, as well as facility capacity upgrade and expansion under various social-economic, ecological, environmental and system-reliability constraints. It could also provide interval solutions with a minimized system cost, a maximized system reliability and a maximized power-generation demand security. Moreover, the developed model could provide an in-depth insight into various CO2 mitigation technologies and the associated environmental and economic implications under a given reduction target. Tradeoffs among system costs, energy security and CO2 emission reduction could be analyzed.
The implementation of large-scale carbon capture and storage (CCS) in the power and industrial sectors will require substantial financial investment. Consequently, research is needed to identify cost effective CCS deployment strategies. This project combines a CO2 pipeline optimization tool, technoeconomic models for CCS components, and regional spatial data to examine how CCS infrastructure might develop in the southwestern United States under the American Power Act (APA). (C) 2011 Published by Elsevier Ltd.
Through combining insights from engineering, natural sciences, economics, and political science, one consistent, transparent, and comprehensive analytical framework for assessing and evaluating various CCS chains is developed. The presented methodology aims at improving knowledge on the design of efficient CCS chains by developing methods for assessing and comparing different CCS chains, their sensitivity with regard to internal factors and to external conditions, and what the most efficient policy tools and measures are for promoting CCS development.
This Intergovernmental Panel on Climate Change (IPCC) Special Report provides information for policymakers, scientists and engineers in the field of climate change and reduction of COâ emissions. It describes sources, capture, transport, and storage of COâ. It also discusses the costs, economic potential, and societal issues of the technology, including public perception and regulatory aspects. Storage options evaluated include geological storage, ocean storage, and mineral carbonation. Notably, the report places COâ capture and storage in the context of other climate change mitigation options, such as fuel switch, energy efficiency, renewables and nuclear energy. This report shows that the potential of COâ capture and storage is considerable, and the costs for mitigating climate change can be decreased compared to strategies where only other climate change mitigation options are considered. The importance of future capture and storage of COâ for mitigating climate change will depend on a number of factors, including financial incentives provided for deployment, and whether the risks of storage can be successfully managed. The volume includes a Summary for Policymakers approved by governments represented in the IPCC, and a Technical Summary. 5 annexes.
Successful large-scale deployment of CCS will require the build-up of commensurate infrastructure to transport CO2 from sources - e.g. power plants - to sinks - e.g. mature oil and gas fields. Research so far has mostly dealt with the techno-economic assessment of pre-defined CO2 value chains, which are typically country-specific and each connect a limited set of sources to a limited set of sinks. By contrast, our paper presents the JRC’s InfraCCS model, a tool that is capable of finding the optimal pipeline-based CO2 transmission network for a given set of sources and sinks. The InfraCCS model is herein applied to the case of CCS in Europe, in order to estimate the strategic benefits of joint optimisation at pan-European level compared to optimisation at the level of individual countries.
Carbon dioxide capture and storage (CCS) links together technologies that separate carbon dioxide (CO2) from fixed point source emissions and transport it by pipeline to geologic reservoirs into which it is injected underground for long-term containment. Previously, models have been developed to minimize the cost of a CCS infrastructure network that captures a given amount of CO2. The CCS process can be costly, however, and large-scale implementation by industry will require government regulations and economic incentives. The incentives can price CO2 emissions through a tax or a cap-and-trade system. This paper extends the earlier mixed-integer linear programming model to endogenously determine the optimal quantity of CO2 to capture and optimize the various components of a CCS infrastructure network, given the price per tonne to emit CO2 into the atmosphere. The spatial decision support system first generates a candidate pipeline network and then minimizes the total cost of capturing, transporting, storing, or emitting CO2. To illustrate how the new model based on CO2 prices works, it is applied to a case study of CO2 sources, reservoirs, and candidate pipeline links and diameters in California.
In the carbon capture and storage (CCS) process, CO2 sources and geologic reservoirs may be widely spatially dispersed and need to be connected through a dedicated CO2 pipeline network. We introduce a scalable infrastructure model for CCS (simCCS) that generates a fully integrated, cost-minimizing CCS system. SimCCS determines where and how much CO2 to capture and store, and where to build and connect pipelines of different sizes, in order to minimize the combined annualized costs of sequestering a given amount of CO2. SimCCS is able to aggregate CO2 flows between sources and reservoirs into trunk pipelines that take advantage of economies of scale. Pipeline construction costs take into account factors including topography and social impacts. SimCCS can be used to calculate the scale of CCS deployment (local, regional, national). SimCCS’ deployment of a realistic, capacitated pipeline network is a major advancement for planning CCS infrastructure. We demonstrate simCCS using a set of 37 CO2 sources and 14 reservoirs for California. The results highlight the importance of systematic planning for CCS infrastructure by examining the sensitivity of CCS infrastructure, as optimized by simCCS, to varying CO2 targets. We finish by identifying critical future research areas for CCS infrastructure.
Considerable attention has been given to carbon dioxide for its impact on climate change. Due to carbon dioxide being emitted from various chemical and petrochemical plants, the gases in geographically scattered places should be gathered, transported and sequestrated. The carbon dioxide disposal network resulting therefrom is a recently emerging issue that should be urgently addressed in the chemical industry in view of the continuously strengthened regulations. This paper proposes a mathematical modeling framework involving carbon capture and storage in order to obtain decision information such as how much captured CO 2 to be held at which sites, and where to be sequestered. The applicability of the proposed framework is demonstrated with potential carbon dioxide networks in Korea, along with some remarks.
Reducing CO2 emissions economically and efficiently necessitates the construction of carbon capture and storage (CCS) infrastructure as a comprehensive network that is capable of disposing of, and of utilizing CO2. Most of the early attempts to design and model the future CCS infrastructure were either limited to examining an individual component of the CCS infrastructure or focused on design and operation of a deterministic, steady-state network in which CO2 emissions are constant over time. In this paper, a multiperiod model for planning CCS infrastructure is developed to consider variation of the emission reduction target, as well as the variation of CO2 emissions over a long-term planning interval, thus leading to phased infrastructure development. The proposed model can help determine where and how much CO2 to capture, store, transport, utilize or sequester for the purpose of maximizing the total profit of the CCS infrastructure while meeting the CO2 mitigation target during each time period of the planning interval. The features and capabilities of the model are illustrated by application of the future CCS infrastructure on the east coast of Korea. The results will be helpful to multiperiod planning of the development of a CCS infrastructure.
A large number of research works were undertaken for the planning of sustainable electricity generation and CO2 mitigation (EGCM) infrastructure design under uncertainty. The typical methodologies assessed the performance of the problem under the variability of the uncertain parameters by optimizing the expected value of the objective function. This approach can have large probabilities of the value optimized in unfavorable scenarios. In this paper, we present a mathematical programming model in planning sustainable electricity generation and CO2 mitigation (EGCM) infrastructure design, including financial risk management under uncertainty. The proposed model allows us to determine available technologies to produce electricity and treat CO2 on the purpose of maximizing the expected total profit and minimizing the financial risk of handling uncertain environments (i.e. CO2 mitigation operating costs, carbon credit prices and electricity prices, etc.), while fulfilling electricity demands and CO2 mitigation standards. The multi-objective optimization problem was solved by using the weighted-sum method that imposes a penalty for risk to the objective function. The capability of the proposed modeling framework is illustrated and applied to a real case study based on Korea, for which valuable insights are obtained.
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 paper we present a decision-support tool to address the strategic planning of hydrogen supply chains for vehicle use under uncertainty in the operating costs. Given is a superstructure of alternatives that embeds a set of available technologies to produce, store and deliver hydrogen. The objective of our study is to determine the optimal design of the production–distribution network capable of fulfilling a predefined hydrogen demand. The design task is formulated as a multi-scenario mixed-integer linear problem (MILP) that considers the uncertainty associated with the coefficients of the objective function of the model (i.e. operating costs, raw materials prices, etc.). The novelty of the approach presented is that it allows controlling the variation of the economic performance of the hydrogen network in the space of uncertain parameters. This is accomplished by using a risk metric that is appended to the objective function as an additional criterion to be optimized. An efficient decomposition method is also presented in order to expedite the solution of the underlying multi-objective model by exploiting its specific structure. The capabilities of the proposed modeling framework and solution strategy are illustrated through the application to a real case study based on Spain, for which valuable insights are obtained.
Commercialization of carbon capture and storage from fossil fuelled power plants requires an infrastructure for transportation of the captured carbon dioxide (CO2) from the sources of emission to the storage sites. This paper identifies and analyses different transportation scenarios with respect to costs, capacity, distance, means of transportation and type of storage. The scenario analysis shows that feasible transportation alternatives are pipelines (on and off shore), water carriers (off shore) and combinations of these. Transportation scenarios are given for different transportation capacities ranging from a demonstration plant with an assumed capacity of 200 MWe (1Mt/y of CO2) up to a system of several large 1000 MWe power plants in a coordinated network (40 Mt/y up to 300 Mt/y of CO2). The transportation costs for the demonstration plant scenario range from 1 to 6€/ton of CO2 depending on storage type and means of transportation. The corresponding figure for the coordinated network scenario with off shore storage is around 2€/ton of CO2.