A Comprehensive Infrastructure Assessment Model for Carbon

Article pubs.acs.org/IECR

A Comprehensive Infrastructure Assessment Model for Carbon Capture and Storage Responding to Climate Change under Uncertainty Jee-Hoon Han and In-Beum Lee* Department of Chemical Engineering, POSTECH, Pohang, Korea S Supporting Information *

ABSTRACT: 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.

1. INTRODUCTION The current economy based on energy production by combustion of fossil fuels emits considerable quantities of greenhouse gas (GHG), especially CO2, which contributes to global climate change. Carbon capture and storage (CCS) will become a profitable alternative to maintain the existing energy production system based on fossil fuels and to reduce CO2 emissions to minimize its influence on global climate change.1 A major challenge for the introduction of CCS technology is the need for a widespread CCS infrastructure to capture, utilize, sequester, and deliver CO2.2 As the demand for reduction in CO2 emissions grows, investment strategies for designing a sustainable CCS infrastructure will be established based on careful analysis that considers critical factors such as economic feasibility, environmental feasibility, and safety feasibility. Our previous research has proposed mathematical models for techno-economic assessment (TEA) of the design and operation of an integrated profit-maximizing CCS infrastructure that includes such activities as capture, utilization, sequestration, and transportation of CO2. These models can be classified into four different categories (i.e., static deterministic,2 static stochastic,3 dynamic deterministic,4 dynamic stochastic5), with regard to their treatment of uncertainty and their decision structure. Although the CCS infrastructure drastically reduces CO2 emissions, it causes other types of environmental pollution (e.g., nitrogen oxides and sulfur oxides).6−8 Thus, the environment assessment (EA) of CCS infrastructure has been addressed in this study.9 Also, CCS infrastructures can entail risks of accidental CO2 leaks.10 Several studies have developed their own technical risk assessment (TRA) approaches, but no © 2013 American Chemical Society

clear guidelines exist regarding how to estimate the safety level required of CCS infrastructure. Moreover, few studies have solved simultaneously analyzed the economic, environmental and safety concerns which must be met if the CCS infrastructure is to be sustainable. Data used to comprehensively analyze CCS infrastructure planning comes from various sources in the literature which performed their own TEA, EA, and TRA; therefore, uncertainties may exist in various factors that affect the CCS infrastructure, including CO2 emission inventories, product prices, operating costs, environment emission inventories, and technical accident inventories. Thus, to obtain reliable results, CCS infrastructure models must consider every possible uncertainty. This analysis requires decision tree analysis, and it is commonly called a stochastic problem. Several research efforts were attempted for considering various uncertainties by introducing a large-scale CO2 mitigation infrastructure. Especially, there have been proposed interval mathematical programming (IMP) and stochastic mathematical programming (SMP) models3,5,11−15 that assess the performance of the problem under the variability of the uncertain parameters typically by optimizing the expected value of the objective function.16 Therefore, the objective of this study is to develop a comprehensive infrastructure assessment model for carbon capture and storage (CiamCCS) that considers (1) the multiple-objective optimization problem (meeting standards of Techno-Economic level, Environmental level, and Technical Safety level) and (2) the effect of uncertainty in input data. The Published: February 19, 2013 3805

dx.doi.org/10.1021/ie301451e | Ind. Eng. Chem. Res. 2013, 52, 3805−3815

Industrial & Engineering Chemistry Research

Article

infrastructure, the proposed model is formulated as a MILP problem. The following subsections discuss objective functions and model constraints in detail. (Notation is given in Table A-1 in Appendix I, which is provided as Supporting Information.) CiamCCS must meet three target requirements simultaneously: (i) maximize expected total economic profit, (ii) minimize expected total environmental impact, and (iii) minimize expected total technical loss. 3.1. Expected Total Economic Profit. An inexact twostage stochastic programming approach is employed in this paper to formulate the techno-economic assessment model under all economic uncertainties such as CO2 emissions, product prices, and operating costs,. Because the stochastic variables associated with the uncertain parameters follow a set of scenarios that each have a given probability of occurrence, the model aims to maximize the expected value of the profit distribution by comparing outputs of a finite set of scenarios over a given planning horizon:

proposed mathematical model is formulated as a static stochastic mixed-integer linear programming (MILP) problem. The model can help to determine where and how to utilize, capture, transport, and sequester CO2 with the goal of simultaneously maximizing expected total economic profit, minimizing expected total environmental impact, and minimizing expected total technical loss while satisfying the mandated reduction of CO2 emissions. To incorporate the multiple-objective optimization problem in the model, we use a fuzzy multiple objective programming approach;17 and to incorporate the uncertainty in the model, we use an inexact two-stage stochastic programming approach.18 We then use the proposed models to examine all possible CCS infrastructures for treating CO2 emitted by burning of carbon-based fossil fuels in power plants throughout Korea in 2020. The proposed model in this study helps decision makers to establish an optimal strategy that balances economy, environment, and safety efficiency against stability in an uncertain future CCS infrastructure.

max E[TEP] =

2. PROBLEM STATEMENT A CCS infrastructure can be considered as a supply network to capture, utilize, sequester, and deliver CO2; this network includes several distinct components, including capture, utilization, and sequestration facilities as nodes, as well as transportation modes that connect them.9 This study proposes CiamCCS to address multiple-objective optimization (meeting Techno-Economic level, Environmental level, Technical Safety level) and to formulate the capture, utilization, sequestration, and transportation planning problem of a supply chain system. The decision-making problem of CiamCCS is to determine the following quantities: (1) the number, location, and type of CO2 capture, sequestration, and utilization facilities in each region considered; (2) the number and type of CO2 transport modes between regions considered; (3) the total amount of capture, sequestration, utilization, and transportation of CO2 under the given conditions, which include CO2 reduction target, capacity limitations of CCS technologies, uncertain parameters (i.e., CO2 emissions, prices, operating costs, environment emission inventories, and technical accident inventories); the overall objective is to simultaneously maximize the expected total profit, minimize the expected environmental impact, and minimize the expected technical loss. This model is based upon the following assumptions: • The networks for CCS infrastructure operate under steady-state conditions, in which a given CO2 reduction pattern is constant over a fixed time horizon. • Economic costs (i.e., CO2 emission inventories, product prices, operating costs), environmental impact (i.e., environment emission inventories) and technical loss (i.e., technical accident inventories) are uncertain. • The remaining parameters are deterministic. In this study, the network design is formulated as a static stochastic MILP model and is compared to a static deterministic model to analyze the effects of different sources of uncertainty separately.

∑ probr TEPr

(1)

r

where r represents a particular scenario and probr is the probability of the occurrence of scenario r. The total economic profit (TEPr) attained in each particular scenario is calculated by the difference between the total annual benefit (TABr) and the total annual cost (TACr). ∀r

TEPr = TABr − TACr

(2)

TABr in each scenario is the income from selling products made by utilizing CO2 in utilization facilities:

∑

TABr =

∑ ∑ USBe ,p,r Pe ,p,g ,r

e ∈ {green polymer, biobutanol}

p

g

(3)

∀r

where USBe,p,r is the unit selling benefit and is regarded as uncertain. TACr in each scenario r is the sum of facility capital cost (FCC), transport capital cost (TCC), facility operating cost (FOCr), and transport operating cost (TOCr): ∀r

TACr = FCC + TCC + FOCr + TOCr

FCC =

⎧ CCR ⎡ ⎪ facility ⎢ ∑ ⎢⎣ e ⎪ LR ⎩

∑⎨ g

+

∑ PCCe ,pBPe ,p,g p

∑ (∑ ∑ ∑ CCCi ,c ,si ,sp,g BCi ,c ,si ,sp,g i

c

si

sp

⎤⎫ ⎪ + ∑ SCCi , sNSi , s , g )⎥⎬ ⎥ s ⎦⎪ ⎭

(5)

TCC = TCConshore + TCCoffshore

(6)

TCConshore =

3. MATHEMATICAL MODEL FORMULATION The mathematical model in ref 9 is revised to accommodate additional considerations of safety and uncertainty. To efficiently optimize the design and operation of the CCS

⎡ CCR pipeline

∑ ∑ ∑ ∑ ∑⎢ i

l ∈ {pipe}

g

g′

d

⎣

LR

⎤ (TPIConi , l , dLonl , g , g ′NTPoni , l , g , g ′ , d)⎥ ⎦ 3806

(4)

(7)



Article

Each term in the right-hand-side of eq 14 is further categorized in terms of a damage indicator (Dn,g,r) in the set of damage categories n, which consist of the damage categories of human health (HH), ecosystem quality (EQ), and resource depletion (RD). They are calculated by summing two impact indicators (i.e., impact indicator models IIn,x,g (for installing a CCS system) and IOn,x,g (for operating it)) in the set of impacts categories x (x ∈ χ:= {HHca, HHro, HHri, HHcc, HHir, HHod, EQtx, EQae, EQlu, RDdr, REdf}).9

∑ ∑

TCCoffshore =

i

l ∈ {pipe}

⎡ CCR pipeline

∑ ∑ ∑⎢ g

g′

d

⎣

LR

⎤ (TPICoffi , l , dLoffl , g , g ′NTPoffi , l , g , g ′ , d)⎥ ⎦

(8)

⎡

FOCr =

⎣

g

+

e

c

si

⎦

s

i

(9)

∀r

(10)

IInk, x , g =

∑ vin,x ,b wibk N gk

l ∈ {pipe}

∀r

IOkn , x , g , r =

d

g′

l ∈ {pipe}

∀r

d

(12)

Capital costs FCC and TCC are calculated by multiplying the number of established CO2 utilization, capture, and sequestration facilities, as well as transportation links, by their capital costs. Operating costs FOCr and TOCr are obtained by multiplying the uncertain parameters unit utilization cost (UPCe,p,r), unit capture cost (UCCi,c,si,r), unit sequestration cost (USCi,s,r), and unit transport costs onshore (UPOConi,l,d,t,r) and offshore (UPOCoffi,l,d,t,r) by their corresponding amounts. In this model formulation, as an inexact two-stage stochastic programming approach, first-stage quantities such as the number of facilities and transport modes cannot change suddenly, but second-stage quantities such as operating capacity of facilities and transport modes are dependent on the expected scenarios. The detailed explanations for the first objective and its constraints were described by Han and Lee.2 3.2. Expected Total Environmental Impact. The second objective function is to estimate the expected total environmental impact E[TEI] of the CCS infrastructure, which consists of the total environmental impact of capture facilities (TEICr), the total environmental impact of sequestration facilities (TEISr), and the total environmental impact of transportation modes (TEITr): min E[TEI] =

∑ probr TEIr r

TEIr = TEICr + TEISr + TEITr

(17)

∀r

∑ von,x ,b wobk ,r Mgk,r 1

∀ n, x , k , g , r

1

(18)

where (i) b1,b2 ∈ B are the sets of the life cycle inventory of installing and operating a CCS facility, respectively; (ii) vin,x,b2 and von,x,b1 are the damage factors of installing and operating a CCS facility so that life cycle inventories b1,b2 contribute to impact category x of damage category n; (iii) ωikb2 is the emissions inventory entry ib per unit number of CCS facility, and ωobk1,r is b1 per unit CO2 flow of CCS technology k; and (iv) Nkg and Mkg are, respectively, the number of CCS facilities and the amount of CO2 flow required for CCS technology k in region g. The main source of uncertainty here is the emissions inventory entry ωokb1,r per unit CO2 flow of CCS technology k, because this quantity cannot be perfectly known in advance during the design stage. Thus, the second objective function must be represented with the second-stage decision variables, Ci,c,si,sp,g,r, Si,s,g,r and Qpipelinei,l,g,g′,d,r, which are associated with ωokb1,r in scenario r:

∑ ∑

∑ ∑ ∑ UPOCoffi ,l ,d ,rQpipelinei ,l ,g ,g ′ ,d ,r g

∀ n, x , k , g

2

b1

(11)

i

2

b2

∑ ∑ ∑ UPOConi ,l ,d ,rQpipelinei ,l ,g ,g ′ ,d ,r TOCoffshorer =

(16)

Here, the weighting factor ϑr,n and normalization ηn are determined from three different perspectives, based on the principles of Cultural Theory.19 If one considers the set of k ∈ 2 technologies such as CCS, the value of impact indicators IIkn,x,g and IOkn,x,g of technology set k can be calculated as

∑ ∑

TOConshorer =

∀r

x

∀r

TOCr = TOConshorer + TOCoffshorer

g′

(15)

Dn , g , r = ηn ∑ (IIn , x , g + IOn , x , g , r )

sp

∑ USCi ,s ,r Si ,s ,g ,r)⎥⎥

g

∀r

p

⎤

+

ϑw , nDn , g , r

w ,n,g

∑ (∑ ∑ ∑ UCCi ,c ,si ,r Ci ,c ,si ,sp,g ,r i

∑

TEIr =

∑ ⎢⎢∑ ∑ UPCe ,p,r Pe ,p,g ,r

TEICr =

∑

(

ϑw , nηn vin , x , b2wic , si , sp , b2 BCi , c , si , sp , g

w , n , x , b1, b2 , i , c , si , sp , g

+ von , x , b1woc , si , sp , b1, r Ci , c , si , sp , g , r

TEISr =

∑

∀r

(19)

(

ϑw , nηn vin , x , b2wis , b2 NSi , s , g

w , n , x , b1, b2 , i , s , g

+ von , x , b1wos , b1, r Si , s , g , r TEITr =

)

)

∑ w , n , x , b1, b2 , i , l , d , g , g ′

∀r

(20)

⎡ ϑw , nηn⎣vin , x , b2wil , d , b2

(13)

(Lonl ,g ,g′NTPoni ,l ,g ,g′,d + Loffl ,g ,g′NTPoffi ,l ,g ,g′,d)

(14)

⎤ + von , x , b1wol , d , b1, r Qpipelinei , l , d , g , g ′ , r ⎦ 3807

∀r

(21)



Article

4. FUZZY APPROACH FOR MULTIOBJECTIVE OPTIMIZATION PROBLEM CiamCCS is mathematically formulated as follows:

The remaining equations in the objective function, which are associated with first-stage variables BCi,c,si,sp,g, NSi,s,g, NTPoni,l,g,g′,d, and NTPoffi,l,g,g′,d, will not change with the scenarios. 3.3. Expected Total Technical Loss. The third objective function is to estimate the expected total technical loss E[TTL] of the CCS infrastructure that results from the activity, and to identify those elements or areas in the system that contribute the most to the total loss. For example, starting with capture, one hazard is the leakage of amines, which may lead to the formation of carcinogenic compounds, which, in turn, can lead to a loss of life or a reduction in the quality of life.20 For both transport and sequestration, one hazard is CO2 leakage, which can lead to a loss of life or environmental damage. Thus, the E[TTL] consists of the total technical loss of capture facilities (TTLcapture ), the total technical loss of sequestration facilities r ), and the total technical loss of transportation (TTLsequestration r modes (TTLtransportation ): r min E[TTL] =

∑ probr TTLr

maximize E[TEP](z , yr ); minimize E[TTL](z , yr )

(23)

∑ τhk,r

∀ k, r (24)

h

The technical accident indicator τkh,r can be expressed as the probability that the technical accident will occur weighted by its cost represented on some numerical (monetary) scale: τhk, r = λhk, r μhk, r

∀ h, k , r

(25)

λkh,r

where is the frequency of occurrence of technical accident category h; μkh,r is the consequence or expected loss of a CCS operator following the occurrence of technical accident category h. The main source of uncertainty here is the technical accident indicator τkh,r, because it cannot be perfectly known in advance during the design stage. Thus, the third objective function can be represented with the second-stage decision variables, Ci,c,si,sp,g,r, Si,s,g,r, and Qpipelnei,l,g,g′,d,r, which are associated with technical accident indicator τkh,r of each technology set k in scenario r: TTLcapture = r

∑

τh , rCi , c , si , sp , g , r

∀r (26)

h , i , c , si , sp , g

TTLrsequestration

=

∑

τh , rSi , s , g , r

∀r

TEIrtransportation =

∑

τh , r Qpipelinei , l , d , g , g ′ , r

f2 (z , yr ) ≤ 0

{Capacity limitations}

Q j = (Q 1 , Q 2 , Q 3) = (E[TEP], E[TEI], E[TTL])

(30)

max Q 1 = E[TEP]

(31)

min Q 2 = E[TEI];

(27)

h,i,s,g

{Overall mass balance}

The objective of this formulation is to find values of the strategic (z ∈ ) and operational (yr ∈ 9 ) decision variables, subject to the set of equality ( f1(z,yr) = 0) and inequality constraints ( f 2(z,yr) ≤ 0). The constraints were described in ref 9. In expression 29, the continuous operational variables (yr) corresponding to scenario r indicate decisions concerned with CO2 capture, sequestration, utilization, and transportation rate, whereas the discrete strategic variables (z) represent investment decisions such as selection of technology types and number of facilities. Meanwhile, we can expect conflicts among the expected total economic profit, the expected total environmental impact, and the expected total technical loss, because an infrastructure that optimizes one of these factors does not necessarily also optimize the others; for example, the infrastructure with the highest profit does not necessarily have the least environmental impact9 nor the least technical loss. Because of these tradeoffs, this type of problem does not have a single solution. Instead, the solution consists of a set of Pareto optimal configurations for the CCS infrastructure. However, we adopt a fuzzy multiple-objective decisionmaking approach,17 because it can provide a clearer theoretical analysis than the weighted-sum method21 and the ε-constraint method,22 which are used most often. Especially, the two-phase fuzzy approach proposed in ref 17 is applied to solve the fuzzy multiple-objective decision-making problems with both fuzzy constraints and fuzzy parameters. It can seek a single compromise solution among the various objectives of a multicriterion decision-making problem. The two-phase fuzzy approach to satisfy the multiobjective optimization problem (i.e., eq 29) proceeds as follows:17 Step 1. Determine the best-case (ideal) solution I* and worstcase (anti-ideal) solution I− by directly maximizing and minimizing each objective function Qj, respectively:

Each term in the right-hand-side of eq 23 is further categorized in terms of technical accident indicators τkh,r in the set of technical accident categories h. If one considers the set of k ∈ 2 technologies such as CCS, the value of technical accident TTLkr of technology set k can be calculated as the following general expression: TTLkr =

f1 (z , yr ) = 0 z ∈ , yr ∈ 9

TTLr = TTLcapture + TTLrsequestration + TTLrtransportation r ∀r

(29)

subject to

(22)

r

minimize E[TEI](z , yr );

min Q 3 = E[TTL]

(32)

I * = (max E[TEP]*; min E[TEI]*; min E[TTL]*)

∀r

(33)

h,i,l ,d ,g ,g′ −

−

−

−

I = (min E[TEP] ; max E[TEI] ; max E[TTL] )

(28)

(34)

The third objective function associated with first-stage variables BCi,c,si,sp,g, NSi,s,g, NTPoni,l,g,g′,d, and NTPoffi,l,g,g’,d, will not differ among the scenarios, because technical accidents cannot be perfectly known in advance during the design stage.

Step 2. Define membership functions for those fuzzy objectives. Using a linear membership function results in a simple scaling of all objective values onto the interval [0, 1]: 3808



Article

⎧1 ⎫ for Q j(z , yr ) ≥ Q *j ⎪ ⎪ ⎪ ⎪ − − Q ( z , y ) Q ⎪ j ⎪ j r − * μj (Q j) = ⎨ for Q j ≤ Q j(z , yr ) ≤ Q j ⎬ − * ⎪ Qj − Qj ⎪ ⎪ ⎪ ⎪0 ⎪ for Q j(z , yr ) ≤ Q j− ⎩ ⎭ ∀j

tion technologies are used (Table 1). Although the detailed superstructure and input data are described in refs 2 and 9, some minor details and changes are described in the next paragraph. Table 1. Capture, Sequestration, Utilization, and Transportation Technologies for the Examined Case Study

(35)

activity capture technology sequestration method utilization method transportation mode

Step 3 (Phase I). Use the minimum operator to obtain the minimum degree of satisfaction ξmin for the worst situation. This phase might result in a noncompensatory solution. In other words, the results obtained by the minimum operator represent the worst situation and cannot be compensated by other members, which may be very good.

max ξ

biobutanol, green polymer liquid CO2 via pipeline onshore and offshore

(36)

Especially, this study examines the effect of uncertain economic profit (i.e., CO2 emission inventories, product prices, operating costs), uncertain environmental impact (i.e., environment emission inventories), and uncertain technical loss (i.e., technical accident inventories) as input data for the design and operation of the CCS infrastructure. The versatility of the proposed model derived (see sections 3 and 4) is examined with two case studies that vary according to the uncertainty types and model types (see Table 2).

subject to ξ≤

type absorption and desorption of carbon dioxide in aqueous monoethanolamine (MEA) depleted gas reservoir (DGR), saline aquifer storage (SAS)

Q j(z , yr ) − Q j− Q *j − Q j−

∀j

where ξ = min(δj(Q j))

(37)

j

Step 4 (Phase II). Obviously, use of a compensatory operator to obtain the compromise solution is more desirable than use of the minimum operator. To avoid noncompensatory solutions, the arithmetical average operator ξave can be used instead of the minimum operator; ξave is the arithmetic average of individual ξ values which correspond to each objective function. This solution is an efficient but nondominated solution; if ξ values are a mixture of high and low values (unbalanced), such as (ξ1, ξ2, ξ3) = (0.94, 0.98, 0.20), the arithmetical average operator results in high performance of objectives or goals, but this is not desirable in compromise programming. To overcome the effects of unbalanced ξ values, a two-phase fuzzy approach is proposed. The minimum operator ξmin is used first in phase I to maximize the satisfaction of the worst situation; then the arithmetical average operator is applied in phase II to maximize the overall satisfaction, with the additional constraint that minimal fulfillment of all fuzzy objectives must be guaranteed. Thus, in phase II, we solve the problem:

Table 2. Analysis Conditions Selected for the Case Study case

1 (∑ ξj) 3 j=1

(38)

Q j(z , yr ) − Q j− Q *j − Q j−

2

economic profit (i.e., CO2 emission inventories, product prices, operating costs), environmental impact (i.e., environment emission inventories), technical loss (i.e., technical accident inventories)

model static deterministic static stochastic

6. RESULTS AND DISCUSSION The proposed model was computed using CPLEX 9.0 (GAMS) on a computer equipped with a Pentium 4 chip, operating at 3.16 GHz. The relatively short times required for solving the two different case studies and the low optimality gaps are satisfactory (see Table 4). 6.1. The Pareto Optimal Solutions. To prove the application and computational effectiveness of the proposed model, we first use a deterministic model for case 1 and a stochastic model for case 2 with the ε-constraint method22 to determine the Pareto set of the multiobjective programming problem. Clear tradeoffs were observed between the expected total economic profit and the expected total environmental impact, and between the expected total economic profit and the expected total technical loss (see Figure 1). Each point of the

subject to ξmin ≤ ξj ≤

no

We collected data about the uncertainty associated with the design and operation of the CCS infrastructure from several sources in the literature which performed TEA,5 EA,6−8,23 and TRA of CCS systems.10 The uncertain parameters can be expressed as probability density functions (PDFs) and discrete interval values; therefore, they are described by 50 scenarios using Monte Carlo sampling, considering a normal distribution with a specified mean and variance (see Table 3). Supporting information for the paper includes additional tables regarding parametric analyses and data estimates (see Appendix I, Tables A.2−A.5).

3

max ξave =

uncertainty

1

∀j

5. CASE STUDY The case study of planning a CCS infrastructure in Korea in 20209 is used to illustrate the applicability of our multiobjective modeling framework. This case study assumes that (i) the levels of mandated reduction of CO2 emissions and utilization of CCS as CO2 reduction technology are 30% and 20%, respectively; (2) gas-fired and coal-fired power plants are the major CO2 emission sources; and (3) several CCS technologies such as CO2 capture, transportation, utilization, and sequestra3809



Article

Table 3. Data Used for Estimating Uncertain Parameters uncertainty source

activity

CO2 emissions

emission emission

product prices

utilization utilization

operating costs

capture

environmental emission inventories

technical accident inventories

type Economic Datab coal-fired power plant gas-fired power plant biobutanol green polymer

MEA in coal-fired power plant capture MEA in gas-fired power plant sequestration DGR sequestration SAS transportation pipeline onshore transportation pipeline offshore Enviromental Datac capture NH3 from MEA in coal-fired power plant capture NH3 from MEA in gasfired power plant capture NOx from MEA in coal-fired power plant capture NOx from MEA in gasfired power plant capture SOx from MEA in coalfired power plant capture energy usage from MEA in coal-fired power plant capture energy usage from MEA in gas-fired power plant sequestration energy usage from DGR sequestration energy usage from SAS transportation pipeline onshore transportation pipeline offshore Technical Datad capture operate MEA in coalfired power plant capture operate MEA in gasfired power plant sequestration operate DGR sequestration operate SAS transportation operate pipeline onshore transportation operate pipeline offshore

mean valuea

Table 4. Summary of Computational Results for the Examined Model

variance (%)

Value

7.9 7.9 20.0 40.9

parameter

Case 1

Case 2

number of constraints number of integer variables number of continuous variables optimality gap (%) CPU time (s)

37 842 664 406 509 0.0 0.09

10 090 460 664 10 861 884 0.0 482.42

20.7

Remarkably, the number of CCS facilities established is requested according to the profit level. To minimize environmental impact and technical loss, the model reduces the number of gas-monoethanolamine (gas-MEA) capture facilities installed. For example, for profit level = 100%, the solution is to set up three new gas-MEA capture facilities, whereas for profit level = 0%, the solution is to establish only one new coal-MEA capture facility. This is because (i) energy consumption, specifically of electricity, is the main contributor to environmental impact scores,9 (ii) the damage factor of energy use from gas resources for operating the MEA facility is 17.5 times that of coal resources,19 and (iii) the technical accident factor (fatalities per unit) for operating the gas-MEA capture facility is 1.58 times that of the coal-MEA capture facility.10 The solutions with the lowest environmental impact and technical loss (profit level = 0%) show a very centralized CCS infrastructure with a large number of pipelines. The result of the same case study (case 2) in which the uncertainty is considered, was compared to that of deterministic Pareto optimal solutions. The number of integer variables in this stochastic case is almost the same as the deterministic case, because they represent the first-stage decisions (i.e., the number of CCS facilities), which are not dependent on the uncertainty. As observed in the deterministic case, a tradeoff between the objectives was observed, because a decrease in the value of the expected total economic profit implies a decrease in both the expected total environmental impact and technical loss in the stochastic case. At profit levels between 80% and 100%, the difference between the expected total economic profit in Pareto solutions of the stochastic model and that of deterministic model increased (i.e., stochastic minus deterministic). However, at profit levels between 0% and 20%, the difference between the expected total environmental impact and technical loss in Pareto solutions of the stochastic model and that of deterministic model increased. These complementary responses are mainly due to the relative elasticity in the uncertainty in the corresponding area: the variability of economic profit parameters has the most direct effect on the configuration of CCS infrastructure at high profit levels, whereas the variability of environmental impact and technical loss parameters has the most direct effect on the configuration of CCS infrastructure at low profit levels. 6.2. Optimal Nondominated and Balanced Solutions. Specifically, in the same case study, we intend to obtain a single nondominated and balanced solution, and to analyze the effect of uncertainty on the CCS infrastructure design. The results of the satisfaction level of each objective (Figure 2) were obtained by using the two-phase approach (see Section 4). We first used the minimum operator to obtain the lowest degree of satisfaction (ξmin) for all objectives and then used the arithmetical average operator to obtain the average degree of satisfaction (ξave). Especially, despite using ξave, the result of

26.6 36.1 43.7 25.0 12.0 87.6 74.1 96.3 60.0 99.6 29.8

11.0

91.9 92.0 63.7 63.7 64.7 64.8 73.7 73.7 81.8 81.8

a

The average value of each uncertain parameter is offered in Appendix I. bThe variance of each uncertain CO2 emission, product price, and operating cost was based on the TEA from ref 5. cThe variance of each uncertain environmental emission inventory value was based on the EA of refs 6−8 and ref 23. dThe variance of each uncertain technical accident inventory was based on the TRA of ref 10.

Pareto set corresponds to a specific CCS infrastructure design (see Table 5). The maximum expected total economic profit solution (profit level = 100%) corresponds to the singleobjective problem that would arise from maximizing exclusively the expected total profit without constraining the expected total environmental impact and the expected total technical loss. In contrast, the minimum expected total economic profit solution (profit level = 0%) is obtained by constraining exclusively the minimum expected total environmental impact and the expected total technical loss. 3810



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Figure 1. Pareto solutions for case 1.

Table 5. The Pareto Optimal Solution of CO2 Infrastructure Designa Profit Level = 0% TEP (× 106 $/yr)b TEI (× 106 points/yr)b TTL (fatalities/yr)b number of capture facilitiesc number of utilization facilitiesd number of sequestration facilitiese number of transportation modes

Profit Level = 20%

Profit Level = 50%

Profit Level = 80%

Profit Level = 100%

D

S

D

S

D

S

D

S

D

S

825 51 0.245 C (1)

862 64 0.254 C (1)

1240 120 0.255 G (1), C (3) GP (1), BB (1) DGR (2)

1251 128 0.258 G (1), C (3) GP (1), BB (1) DGR (2)

1302 162 0.261 G (2), C (1) GP (1), BB (1) DGR (2)

1411 166 0.260 G (2), C (1) GP (1), BB (1) DGR (2)

1568 191 0.261 G (3)

GP (1), BB (1) DGR (2)

1051 89 0.255 G (1), C (2) GP (1), BB (1) DGR (2)

1467 189 0.265 G (3)

GP (1), BB (1) DGR (2)

1042 79 0.249 G (1), C (2) GP (1), BB (1) DGR (2)

GP (1), BB (1) DGR (2)

GP (1), BB (1) DGR (2)

pipe (7)

pipe (7)

pipe (5)

pipe (5)

pipe (4)

pipe (5)

pipe (6)

pipe (4)

pipe (4)

pipe (4)

a D = deterministic model for case 1; S = stochastic model for case 2. bTEP = total economic profit, TEI = total environmental impact, TTL = total technical loss, especially the expected value in the stochastic model. cCapture facility: G = MEA in a gas-fired power plant, C = MEA in a coal-fired power plant. dUtilization facility: GP = green polymer, BB = biobutanol. eSequestration facility: DGR = depleted gas reservoir, SAS = saline aquifer storage.

case 2 (stochastic model) shows that the satisfaction levels are well-balanced, whereas the result of case 1 (deterministic model) shows that the satisfaction levels are extremely unbalanced. After using the minimum operator in phase I to find the maximum satisfaction for the worst situation, the arithmetical average operator was applied in phase II with a guaranteed satisfaction level for all fuzzy objectives. Compared to other fuzzy approaches, the two-phase approach achieves a greater increase in average satisfaction level of each objective for case 2 over that of case 1. However, the result of case 2 shows that the satisfaction levels are more unbalanced than in case 1, because the difference in the variability of input data

changes not only the design and operation of CCS facilities but also the value of each objective. First, the two-phase approach was used to provide the economic profits, environmental impact scores, and technical loss scores results of the same case study when using the deterministic model and the stochastic model (see Table 6). The stochastic model considering the uncertainty resulted in a 5.67% decrease in total economic profit and in a 1.19% decrease in total technical loss, but a 7.30% increase in total environmental impact. These results imply that a tradeoff exists between the total economic profit and the environmental impact score in both cases. In both cases, the largest contribution to changes in total economic profit was the 3811



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Figure 2. Comparison of satisfaction level of each objective for CCS infrastructure by a two-phase fuzzy decision-making method.

infrastructure planning considering the uncertainty is sensitive to the overall economic, environmental, and technical levels of CO2 capture technologies, while the environmental level of CO2 sequestration facilities, and the economic and technical levels of CO2 transport links. The computation results of the two-phase approach also provide all decision variables, such as the capture, sequestration, utilization, and transportation configuration plan, to achieve the goal of a fair balance of objectives. Optimized CCS

capital cost of CO2 transport links, and the second largest was the operating cost of CO2 capture facilities (see Table 6). In total environmental impact, the largest contribution to changes was the ecosystem quality damage caused by CO2 sequestration facilities and the second largest was the human health damage caused by CO2 capture facilities. In total technical loss, the largest contribution to changes was the accidents caused by CO2 transport links and the second largest was safety accidents caused by CO2 capture facilities. These results show that CCS 3812



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Compared to the result of case 1, the result of case 2 is a more decentralized CCS infrastructure with considerably more large-scale transportation links, despite their economic, environmental, and safety burdens. This is mainly because the mandated reduction of uncertain CO2 emissions was satisfied by installing additional CO2 capture facilities or transportation links, just in case the CO2 emissions are above average in a specific scenario. However, the optimal solution for CO2 sequestration and utilization facilities did not differ significantly between the two cases. Both cases prefer CO2 disposal activities such as depleted gas reservoir (DGR) sequestration or utilization activities such as production of biobutanol and green polymers. This is because the economic profit, environmental impact score, and technical loss score of CO2 sequestration and utilization facilities under the uncertainty are regarded as less important factors than those of capture facilities and transportation links. We can propose switching from MEA capture facilities in coal-fired power plants to gas-MEA capture facilities at the design step to meet the economic, environmental, and safely requirements for the future CCS infrastructure that can respond to climate change under uncertainty. Also, the decentralized transportation mode network is more favorable than the centralized one, at least in Korea.

Table 6. Economic Profits, Environmental Impact Scores, and Technical Loss Scores of CCS Infrastructure Planning for Deterministic Model and Stochastic Model CO2 Reduction Target: 1.5 × 107 tCO2/yr deterministic model

stochastic model

Economic Profits (million $/yr) Benefits utilization 4360 4456 total benefits 4360 4456 Capital costs capture 800 816 utilization 214 214 sequestration 15 15 transportation 79 135 total capital costs 1109 1181 Operating costs capture 413 507 utilization 1577 1576 sequestration 25 26 transportation 37 34 total operating costs 2053 2142 Total economic prof its 1200 1132 Environmental Impact (Points/yr) Human health capture 16 743 801 17 508 818 transport sequestration total human health 16 743 801 17 508 818 Ecosystem Quality capture 4 921 141 4 866 387 transport 2944 2944 sequestration 3 265 713 6 937 218 total ecosystem quality 8 189 798 11 806 548 Resources capture 69 495 800 72 245 155 transport 844 132 872 869 sequestration 3 790 579 3 861 908 total resources 74 130 511 76 979 932 Total environmental 99 064 110 106 295 299 impact Technical Loss (Fatalities/yr) Safety accident capture 0.042 0.044 transport 3.6669 × 10−5 6.7659 × 10−5 sequestration 0.210 0.205 total technical loss 0.252 0.249

change (%)

2.20 2.20 2.00 0.00 0.00 70.89 6.49 22.76 −0.06 4.00 −8.11 4.34 −5.67

7. CONCLUSIONS This paper has introduced a comprehensive infrastructure assessment model for carbon capture and storage (CiamCCS) which integrates the major assessment methods of carbon capture and storage, i.e., techno-economic assessment (TEA), environmental assessment (EA), and technical risk assessment. The model also applies an inexact two-stage stochastic programming approach to consider the effect of every possible uncertainty in the 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). To maximize the expected total economic profit, minimize the expected total environmental impact, and minimize the expected total technical loss, the proposed model is formulated as a static stochastic MILP problem, which determines where and how much CO2 to capture, transport, sequester, and utilize. To implement this concept, a fuzzy multiple objective programming was used to attain a compromise solution among all objectives of the CiamCCS. It is an ambitious attempt to include a large set of integrated multiobjective decisions within a single mathematical modeling framework that considers uncertainties. The proposed model was used to examine its ability to design and operate optimal CCS infrastructures for treating CO2 emitted by the use of carbon-based fossil fuels in power plants on the entire region of Korea in 2020. Simulation results indicate a clear tradeoff between the expected total economic profit and the expected total environmental impact, and between the expected total economic profit and the expected total technical loss. Specifically, to minimize the environmental impact and technical loss, the model resolves to reduce the number of gas-MEA capture facilities. This is because (i) energy consumption, specifically electricity, required to capture CO2 is the main contributor to environmental impact scores and the energy use in a gas-MEA capture facility is more significant; and (ii) the technical accident factor (fatalities per unit) for operating a gas-MEA capture facility is 1.58 times

4.57 0.00 0.00 4.57 −1.11 0.00 112.43 44.16 3.96 3.40 1.88 3.84 7.30

4.76 84.51 −2.38 −1.19

configurations were obtained for both cases (Figure 3), including the number, type, and capacity of capture and sequestration facilities installed in each region along with the selected transportation modes among them. Particularly, the location and number of CO2 capture facilities and transportation links differed between the cases, whereas the location and number of CO2 sequestration and utilization facilities were similar in the two cases. For example, the deterministic case mainly uses aqueous MEA capture facilities in coal power plants, whereas in the stochastic case, they are installed in coal power plants together with more gas-MEA facilities. This implies that the gas-MEA facility is better than the coal-MEA facility economically, environmentally, and safely under uncertain environments for the future CCS infrastructure. 3813



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Figure 3. Optimal solution of the CCS infrastructure design.

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higher than that of a coal-MEA capture facility. However, in view of stability under uncertain environments, a gas-MEA capture facility is better than a coal-MEA capture facility economically, environmentally, and safely, because of the lower variability of input data in the gas-MEA capture facility than in the coal-MEA capture facility. Also, the decentralized transportation mode network is more favorable than the centralized one, at least in Korea. In contrast, the optimal design of CO2 sequestration and utilization facilities was relatively independent of the variation of input data. We can also propose the optimal operation of each CCS infrastructure as well as each power plant under uncertainty. These characteristics of the model can help to increase our knowledge of how to design and operate optimal CCS infrastructures capable of responding to uncertainties in realistic problems.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +82-54-279-2274. Fax: +82-54-279-5528. E-mail: iblee@ postech.ac.kr. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This paper was supported by the Korea Research Foundation Grant funded by the Korea Government (MOEHRD, Basic Research Promotion Fund) (No. KRF-2008-313-D00178).

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Appendix I is provided as supporting information; this appendix includes tables that present parametric analyses and data estimates. This material is available free of charge via the Internet at http://pubs.acs.org. 3814



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A Comprehensive Infrastructure Assessment Model for Carbon

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