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A meta-analysis of carbon capture and storage technology assessments: Understanding the driving factors of variability in cost estimates
Applied Energy 159 (2015) 11–18
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
A meta-analysis of carbon capture and storage technology assessments: Understanding the driving factors of variability in cost estimates Oguz Akbilgic a,b, Ganesh Doluweera b,1, Maryam Mahmoudkhani c,2, Joule Bergerson b,d,⇑ a
UTHSC-ORNL Center for Biomedical Informatics, 910 Madison Ave., Memphis, TN 38163, USA Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada c Department of Energy and Environment, Chalmers University of Technology, Sweden d Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada b
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
Meta-analysis to explain
discrepancies in previous studies assessing CCS costs. Regression models have strong predictive power (R2 > 0.90). Capital cost & relative thermal efficiency penalty have largest impact on cost. First analysis to quantify the contribution of parameters to variability of CCS cost. Analysis can be used to make future cost studies more transparent and comparable.
a r t i c l e
i n f o
Article history: Received 6 May 2015 Received in revised form 13 August 2015 Accepted 15 August 2015 Available online 8 September 2015 Keywords: Carbon capture and storage Meta-analysis Cost of carbon abatement Power plant cost estimation Regression analysis
a b s t r a c t The estimated cost of reducing carbon emissions through the deployment of carbon capture and storage (CCS) in power systems vary by a factor of five or more across studies published over the past 8 years. The objective of this paper is to understand the contribution of techno-economic variables and modeling assumptions to explain the large variability in the published international literature on cost of avoided CO2 (CACO2) using statistical methods. We carry out a meta-analysis of the variations in reported CACO2 for coal and natural gas power plants with CCS. We use regression and correlation analysis to explain the variation in reported CACO2. The regression models built in our analysis have strong predictive power (R2 > 0.90) for all power plant types. We find that the parameters that have high variability and large influence on the value of CACO2 estimated are levelized cost of electricity (LCOE) penalty, capital cost of CCS, and efficiency penalty. In addition, the selection of baseline technologies and more attention and transparency around the calculation of capital costs will reduce the variability across studies to better reflect technology uncertainty and improve comparability across studies. Ó 2015 Elsevier Ltd. All rights reserved.
⇑ Corresponding author at: Department of Chemical and Petroleum Engineering, University of Calgary, EEEL Building, Room 461B, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada. Tel.: +1 403 220 5265; fax: +1 403 220 2400. E-mail address: [email protected] (J. Bergerson). 1 Current address: Canadian Energy Research Institute, #150, 3512 - 33 Street NW, Calgary, AB T2L 2A6, Canada. 2 Current address: ConocoPhillips, Canada. http://dx.doi.org/10.1016/j.apenergy.2015.08.056 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
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1. Introduction In many regions globally, a coal-fired power generation plant produces the cheapest reliable and dispatchable electricity [1]. However, these plants are being challenged by concerns about conventional air pollutants and greenhouse gas emissions (GHG). This has lead to a decrease in their use over the past 5 years. For example, in the U.S., coal-fired electricity production decreased by 20% from 2000 TWh in 2008 to 1600 TWh in 2013 [2]. One technological option that can enable continued use of coal-fired plants in a carbon constrained economy is carbon capture and storage (CCS) [3,4]. However, the estimated cost of reducing carbon emissions through the deployment of CCS in power generating units is prohibitively high in many jurisdictions. To address the challenge of prohibitively high costs, advanced capture technologies are being investigated that could greatly reduce the cost of capture. Another challenge is that the magnitude of the costs estimated (even for existing technologies) can vary by a factor of five or more. This variability undermines support for policies to curb carbon emissions through deployment of CCS. That is, private energy investors tend to be reluctant to adopt a new technology without an understanding of its true costs. Therefore, this must be addressed prior to further establishment of policies or technology development directives. The variability in costs is often interpreted as the inherent uncertainty in the technology. In fact, it is likely that much of the variability across studies is due more to the modeling assumptions and boundaries of analysis than the technical uncertainty of how the technology will perform once deployed at commercial scale [5,6]. Variability in CCS cost and performance estimates has been studied previously with four main categories of literature discussed below. The first category includes studies that review CCS costs and trends as part of status updates on the technologies (e.g., [7]) and potential applications of CCS in specific geographic regions (e.g., [8,9]). The second category includes studies that review CCS costs as the basis of comparison of competitiveness against other low-carbon technologies (e.g., [10]). These two categories of studies demonstrate the need for consistency in conducting cost studies. The third category includes studies that attempt to understand the magnitude and impact of different assumptions and methods related to CCS costing. Schreiber et al. [11] conducted a metaanalysis of 15 environmental life cycle assessments of CCS technologies. However, they were unable to draw robust conclusions about the magnitude of the environmental impacts due to the variability in parameters and study boundaries used across the studies [11]. A comprehensive review of CCS costs and performance parameters has been made by Li et al. in [12], revealing wide ranges of important parameters. These include capital costs and plant efficiency penalty due to the capture process. Another review by Rubin developed ranges of cost estimates for different CCS technologies. This study revealed that significant differences exist in CCS cost estimation methods employed by leading government and industry organizations [5]. These differences are not only parametric such as the variability in key CCS technical, economic and financial assumptions but also structural such as the choice of metrics to estimate CO2 emissions abatement cost and the elements of cost that are included or excluded [5]. Jones [13] evaluates key trends in CCS cost estimates over time. While one of the objectives of the study was to ‘‘understand the drivers underlying the key trends”, this was done in a qualitative fashion. Allinson et al. [14] reviewed several methods used to estimate costs of CCS and highlighted the differences that can lead to variability. They also conducted a basic sensitivity analysis by varying a subset of parameters one at a time. They acknowledge that the combined sensitivities are important but they didn’t quantify these impacts. In [15], Nemet et al. employ expert elicitation and Monte-Carlo
simulation to construct probability distributions of carbon abatement costs for different CCS technologies. A wide range of CCS cost and performance estimates utilized for the Monte-Carlo simulation was found. These are due to differences in expert opinions and variability in CCS technology parameters found in published literature. The fourth category includes studies that make recommendations and propose guidelines for how to perform cost estimates of CCS. For example, ICO2N [16] described a high-level set of guidelines related to the types of parameters to include, the boundary of analysis etc. Rubin et al. [17] outlined a very thorough and extensive set of guidelines on methods that can be used to harmonize both CCS cost estimates as well as their communication to different audiences. These guidelines are informed by extensive experience in conducting cost estimates and reviewing other CCS cost studies. However, the paper does not quantify the relative contributions of the individual parameters to explain the discrepancies in CCS costs in a quantitative manner. The aforementioned studies provide evidence of the variability of CCS costs and performance estimates. However, none of these studies takes a quantitative approach to assessing the relative contribution of each parameter in explaining the variability seen across CCS cost studies. In this paper, we develop an analysis to understand and quantify the nature of the variability and the influence of relevant parameters on CCS cost estimates. This is the first study of its kind that deploys a modeling approach that explains the variability in cost estimates by incorporating the variability in the input parameters simultaneously. This analysis results in a better understanding of the source, nature, and magnitude of this variability to distinguish between what is due to operating and modeling decisions compared with inherent uncertainty about the technology itself. This can then be used to support and better inform the priorities around the parameters that should be included and communicated in future CCS cost studies. This analysis can, therefore, be used to help reduce variability due to modeling details in CCS cost estimates by quantitatively assessing the main driving factors of this variability. In addition, by understanding the source of the variability, we develop a better understanding of how to reduce this variability in future studies and make better comparisons across studies. We conduct a meta-analysis and regression analysis to understand the underlying relationships between parameters used to estimate the cost of CCS. Meta-analysis is a group of statistical techniques for combining and integrating the quantitative results from several independent studies. This can be used to explain the differences between studies and a more broadly based estimate of the existence, size and reliability of relevant effects. Metaanalysis, therefore, extends beyond a standard literature review by analyzing and synthesizing the results of multiple studies in a statistical manner [18–20]. We collect a set of observations consisting of input parameters and results of different studies that estimate the cost of CCS. We then use exploratory statistical analysis to explain variability in CCS cost estimates in terms of the model parameters most commonly used.
2. Methods Our meta-analysis includes a review of over 40 technical reports and articles. These studies report cost estimates of coal- and natural gas-fired power plants in North America and OECD Europe. Most have been published by leading government, industrial, and academic institutions involved in developing and/ or deploying CCS technologies. The studies were selected based on their scope and availability of all data required for the meta-analysis.
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In this paper, we focus on three types of power plants with CCS; namely, Pulverized Coal (PC), coal-fired Integrated Gasification Combined Cycle (IGCC) and Natural Gas Combined Cycle (NGCC). We collect the technology cost and performance data for the analysis from 15 studies that report all data used in the analysis (57 sets of observations: 21 for PC; 20 for IGCC; and 16 for NGCC) [1,12,21–33]. For the full dataset used for this analysis, see Tables S1–S3 of supplementary information (SI). Of the 21 observations pertaining to PC power plants, the technologies considerations are: 18 supercritical pulverized coal power plants; 2 ultra- supercritical pulverized coal power plants; and 1 sub-critical pulverized coal power plant. There are many ways to express the cost of avoiding CO2 emissions from CCS applications. These include the cost of CO2 abated (CACO2) and cost of CO2 captured [5]. We select CACO2 for our analysis because it is the most commonly reported cost of reducing CO2 using CCS. Furthermore, CACO2 captures CO2 associated with both the net output energy and parasitic energy consumption of the capture process. As such, comparisons based on CACO2 incorporate the benefits of efficient capture technologies. CACO2 also allows for a more direct comparison with other CO2 mitigation technologies. CACO2 compares a plant with CCS to a baseline (or reference) plant and calculates the average cost of avoiding emissions of a unit mass of CO2 (typically measured in $ per metric ton of CO2 avoided) [5]. By definition, baselines must be identified to determine CACO2 of the CCS power plant under study. Defining the baseline is difficult as there’s no clear ‘best answer’ [1,5]. A CCS project should be compared to a system producing an equal quantity of the same product (e.g., electricity) to what would be built in the absence of the CCS plant. Most often, several options exist for producing any given product. For example, power can be produced using fossil-based options such as PC, IGCC, and NGCC technologies. However, it can also be produced using low carbon options such as nuclear or renewable energy technologies. Therefore, any one of these options could be considered the baseline for a new power plant with CCS. The baseline should be determined for a specific jurisdiction based on the technology that is most likely to be built in the absence of CCS technologies (e.g., resource availability, incentives such as Renewable Portfolio Standards). It is interesting to note that the baseline plant of each and every study we extracted data from is the plant under study without CO2 capture. This indicates that even though large variability exists, it might not capture the full variability nor the true costs of avoided CO2 in all applications/jurisdictions. For a given power plant with CCS, CACO2 is calculated by Eq. (1), where LCOE is the levelized cost of electricity in $/MWh. For our analysis, we calculated CACO2 using Eq. (1) for studies that did not explicitly report CACO2.
CACO2 ¼
LCOEPlant with CCS LCOEBaseline plant ðCO2 emissions=MWhÞBaseline plant ðCO2 emissions=MWhÞPlant with CCS ð1Þ
LCOE and CO2 emissions of a power plant are determined by its capital cost, fuel price, efficiency, annual utilization, CO2 content of the fuel, and financial assumptions such as the economic life of the project and the discount rate. Therefore, in estimating CACO2, there are many variables that affect the estimate of cost. When selecting the relevant variables for our analysis we hypothesize that the following parameters have the largest influence on the CACO2 of CCS technologies. In general, capital cost represents the largest cost associated with a power plant. The influence of capital cost is represented in our analysis by capital cost of CCS (CCccs), capacity factor (CF), and capital charge factor (CCF). Fuel cost, in general, is the second largest cost and it is represented
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by fuel (coal and natural gas) price (FP) and thermal efficiency of the CCS power plant (Effccs). Project economic life and discount rate are collectively represented by the capital charge factor (CCF). Since CACO2 is measured with reference to a baseline, it is important that we incorporate the influence of the baseline plant. LCOEpen is the relative increase in LCOE of the CCS power plant compared to its baseline power plant. Similarly, the efficiency penalty (Effpen) is the relative decrease in CCS power plant efficiency due to power consumed by the CO2 capture and compression units compared to its baseline [34]. The latter two parameters represent the influence of the baseline power plant on CACO2. In addition to their influence on the costs of CCS, Effccs and Effpen also determine the CO2 emissions associated with the CCS power plant (i.e., they influence both the numerator and denominator of Eq. (1)). Therefore, they may have a larger influence on CACO2. Furthermore, reduction in power plant efficiency resulting from the CO2 capture process is touted to be a major barrier for CCS technologies [34]. CCS cost estimates in the studies we include in our analysis have been made over different time periods and for different jurisdictions. Therefore, the year and the jurisdiction of the estimate are two additional parameters that may influence CACO2. However, we do not investigate the influence of the jurisdiction on CACO2 due to insufficient data from different jurisdictions. The majority of the cost data we collected are for the U.S. and European Organisation for Economic Co-operation and Development countries. Therefore, our insights are directed to these jurisdictions. Furthermore, in our analysis, CACO2 is considered a variable that is a linear combination of the other variables. Thus, the year of the estimate is indirectly taken into account via the other variables considered. Finally, we did test the influence of these variables in our regression model using stepwise linear regression (see Table S8 of SI). Neither variable was selected in the model, which means they don’t significantly contribute to the explanation of variability in the dependent variable. 2.1. Assessment of parametric variability The variables considered in our analysis are populated with the input values in the original models or calculated based on the primary parameters presented in the articles/reports. For one study [22] we calculate the cost of electricity (assuming a discount rate of 10% and project economic life of 30 years) and CACO2. We start our analysis by visually assessing the variability of different variables using box plots. We calculate descriptive statistics such as min, median, mean, max, and standard deviation. We use the coefficient of variation (the ratio between the standard deviation and the mean of a given variable) as a measure of variability of each variable. Since the variables we use have different units, a normalized measure such as coefficient of variation allows us to directly compare the relative variability of respective variables. We also calculate correlation coefficients between CACO2 and each independent variable. 2.2. Assessment of the impact of different variables on CACO2 To investigate what drives the differences between CCS cost estimates in the literature, we employ correlation analysis. Correlation analysis is one of many methods that can be used to investigate the nature and strength of the relationship between two or more variables. In order to conduct a correlation analysis, first we build a regression model [35]. In regression, the statistical relationship between independent variables and the dependent variable is assumed to be linear [35,36]. Therefore, a linear regression model is simple and easy to interpret. When linearity is satisfied, the parameters of the linear regression model expose the individual influence of each independent variable. In this paper,
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we build a multiple linear regression (MLR) model to study the statistical relationship between CACO2 and the other model variables. Our goal here is not only to predict CACO2 but also to understand which model variables explain the most variation in CACO2. There are many ways to assess the goodness of fit of a statistical model (i.e., how well a model fits data and the validity of a model [35]). We use the R2 statistic to measure how well our linear model estimates CACO2 using a linear combination of the other variables as inputs. Although we are aware of the non-linear nature of the relationship between CACO2 and several of the variables considered in our analysis, we assume that the non-linear part of the relationships are negligible. Therefore, they can be excluded from our model. The goodness of fit of the models, R2, is then used to decide if the linear regression model is sufficient and confirm that the nonlinear terms can be neglected. We also use variable selection techniques to determine the most significant predictors within our models. We use stepwise regression (systematically adding and removing independent variables) to create several models and test their relative performance using the F-test and t-test [36]. The MLR model we build is given in Eq. (2).
CACO2 ¼ b0 þ b1 FP þ b2 CCF þ b3 CF þ b4 CCCCS þ b5 Eff CCS þ b6 LCOEpen þ b7 Eff pen þ e
ð2Þ
In Eq. (2), b0 b7 are the regression coefficients and e Nð0; r2 Þ is the random error term. We estimate b0 b7 and r (standard deviation) by utilizing ordinary least squares based regression model fitting techniques [36]. When independent variables are measured in different units, standardized regression coefficients (standard beta coefficients) are needed to compare the relative importance of independent variables. Standard beta coefficients are calculated by first standardizing the independent variables so that their variances are unity, and then by fitting the MLR model given in Eq. (2) using standardized independent variables. In our problem, standard regression coefficients show the variation in CACO2 corresponding to one standard deviation increment of the corresponding independent variable. 2.3. Synthesizing variability analysis and influence analysis Finally, to derive insights to inform future CCS cost assessments, we combine the results of the parametric variability analysis and the assessment of the impact of different variables through MLR modeling. We examine the calculated coefficient of variation and standard beta coefficients to identify variables that have a large overall impact on CCS costs. That is, higher variability and higher impact on CACO2. We also examine the studies we reviewed for structural variations in methods employed for CCS technology assessments. 3. Results and discussion 3.1. Parametric variability and correlation The data extracted from 15 studies are summarized in Fig. 1 by technology type for six independent variables and one dependent variable (CACO2). Descriptive statistics associated with this data are presented in Table 1. The table also summarizes the degree of variability (Coefficient of variation) and the degree of correlation between each input parameter and CACO2 (correlation with CACO2). The spread in CACO2 across observations for PC coal power plants is large; the minimum and maximum estimates are 34 and 112 $/tonne of CO2 respectively. We observe the same trend for the other two types of plants, IGCC and NGCC, (i.e. a minimum
of $20 and maximum of 114 $/tonne of CO2 for IGCC plants, and a minimum of 45 and maximum of 224 $/tonne of CO2 for NGCC plants). The calculated coefficient of variation (i.e., standard deviation as a percentage of the mean) of CACO2 for PC, IGCC, and NGCC plants are 34%, 52%, and 51% respectively. It is interesting to note that while the variability in the estimate of CACO2 is high for all three technologies, it is lowest for PC plants. While the variation of FP is high for all plant types, it is higher for coal, which is surprising. This is likely due to regional variations of coal prices compared to natural gas, which is generally more consistent across regions but is more volatile. The correlation coefficients between CACO2 and each independent variable are calculated and reported in Table 1. The correlation coefficient for each variable indicates that CACO2 has a strong correlation (i.e., correlation coefficient greater than 0.6) with two independent variables: LCOEpen and CCccs for all three power plant types. However, for IGCC power plants, the Effpen variable is also strongly correlated with CACO2. This is likely due to the fact that the Effpen impacts both the numerator (LCOE differential) and the denominator (emissions differential) of Eq. (1), which is used to calculate CACO2. Effpen might be significant for IGCC plants and not for the other two technologies because the relative capital cost difference between the IGCC plant with and without CCS is smaller than that for PC and NGCC plants. Consequently, the difference in fuel consumption between the IGCC plant with and without CCS is larger and has a higher impact on CACO2 than that for the other two technologies. For all plant types, the correlations of other variables with CACO2 are not significant. 3.2. Impact of independent variables on CACO2 Here, we analyze the statistical relationship between the cost of avoided CO2 (CACO2) and seven independent variables using linear regression analysis. We carry out our analysis separately for each power plant. The regression coefficients derived from the analysis are presented in Table S6 of the SI. Note that all three regression models have very strong explanatory power (PC R2 = 0.90; IGCC R2 = 0.93; NGCC R2 = 0.98). That is, according to the regression models, the variability in the cost of captured CO2 is very well represented with the linear models using the variables considered in this study. This also confirms that the nonlinear effects are not required since the linear models explain more than 90% of the variability. Although all three regression models are significant, several of the regression coefficients are not. In other words, some of the independent variables do not make a significant contribution to the explanation of the variation in CACO2 in the linear regression model. Stepwise regression techniques expose the key determinants of the cost of avoided CO2 (See Section S3 & Table S7 of SI). This analysis shows that the subset of independent variables that best explains the variability in the CACO2 includes CCccs, LCOEpen, and Effpen for all plant types (i.e., are significant variables). Since these three variables are the only variables in the stepwise regression models for IGCC and NGCC plants, we can conclude that IGCC and NGCC power plants are similar in terms of the determinants of CACO2. However, in addition to CCccs, LCOEpen, and Effpen, CF also plays an important role in PC power plants. This is confirmed by comparing the standard betas of each variable for each power plant type (see the SI for details). Standard betas also quantify the magnitude of the impact of each important variable. LCOE is another important parameter to estimate CACO2. However, LCOE is not directly included as a predictor in the regression analysis since it can be explained by the other variables that are already in the model (See Section S2 of SI for further support of this hypothesis).
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Fig. 1. Distributions of avoided cost of CO2 estimates as well as variables that affect CACO2 across studies for three types of CCS technologies. Data points are marked as ‘s’. For a given data set, the bottom and top edges of the boxes correspond to the 25th and 75th percentile respectively. The solid horizontal line inside the box corresponds to the median. The upper whiskers extend to the maximum data point value that is within 1.5 times the interquartile range from the top edge of the box. The same is true for the lower whiskers. Outlier data points are marked as ‘X’. Colors represent different technologies: Blue is pulverized coal (PC), red is coal-fired integrated gasification combined cycle (IGCC) and green is natural gas combined cycle (NGCC). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.3. Synthesizing variability analysis and influence analysis In Fig. 2 we examine the magnitude of variability and the level of influence of the parameter in explaining the CACO2 simultaneously. The LCOE penalty and efficiency penalty are both highly variable and have a large influence on CACO2 (both have large coefficients of variation and standard beta values) for all three technologies. This reaffirms the importance of careful selection of a baseline for CACO2 estimates. This is due to the fact that these parameters are calculated by comparing the performance of a plant with CCS to a baseline plant that would be built in the absence of the CCS option. The baseline plant is a structural choice made by
the developer of the CCS cost estimation model and, therefore, has an impact on the variability and magnitude of CACO2. When selecting a baseline there is no ‘best answer’ and the choice should be made taking into account the context within which the CACO2 estimate is made. CCF is often touted to be highly influential in determining avoided cost. Surprisingly, it is neither highly variable nor highly influential across the studies investigated. It is also interesting to note that the influence of CCF is lower for IGCC than it is for PC or NGCC plants. This is in spite of the fact that the technological risks associated with IGCC generally tend to be considered higher (i.e., CCF is often used to represent the level of technological risk
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Table 1 Descriptive statistics and correlations of model variables. Technologies included are pulverized coal (PC), coal-fired integrated gasification combined cycle (IGCC) and natural gas combined cycle (NGCC). Statistics included are capital cost of CCS (CCccs), capacity factor (CF), capital charge factor (CCF), fuel (coal and natural gas) price (FP), thermal efficiency of the CCS power plant (Effccs), capital charge factor (CCF), the relative increase in levelized cost of electricity of the CCS power plant compared to its baseline power plant (LCOEpen) and the relative decrease in CCS power plant efficiency due to power consumed by the CO2 capture and compression units compared to its baseline (Effpen Numbers in bold indicate strong correlations.). Statistics
Plant type
FP ($/GJ)
CCF (/year)
CF
CCccs ($/kW)
Effccs
LCOEpen (%)
Effpen (%)
CACO2 ($/tCO2)
Min
PC IGCC NGCC
1.0 1.0 3.3
0.09 0.09 0.09
0.75 0.75 0.75
1838 1815 869
0.26 0.29 0.34
40 24 28
15 12 14
34 20 45
Median
PC IGCC NGCC
2.0 2.1 7.3
0.10 0.11 0.10
0.85 0.85 0.85
3404 3143 1431
0.30 0.31 0.43
60 36 33
25 20 15
59 43 75
Mean
PC IGCC NGCC
2.2 2.2 7.3
0.11 0.11 0.11
0.83 0.83 0.82
3288 3292 1637
0.30 0.32 0.42
68 43 40
25 19 17
61 46 90
Max
PC IGCC NGCC
4.2 4.2 9.5
0.15 0.15 0.15
0.86 0.86 0.86
6560 6600 3750
0.36 0.36 0.45
130 131 98
36 26 34
112 114 224
Standard deviation
PC IGCC NGCC
0.9 0.9 1.9
0.02 0.02 0.02
0.04 0.04 0.05
1155 1289 738
0.03 0.02 0.03
23 23 17
4.4 3.7 6.0
20.5 23.8 45.6
Coefficient of variation
PC IGCC NGCC
38 40 26
17 16 16
5 5 6
35 39 45
9 6 8
34 54 44
18 19 34
34 52 51
Correlation with CACO2
PC IGCC NGCC
0.3 0.4 0.3
0.34 0.40 0.25
0.29 0.46 0.49
0.73 0.66 0.93
0.19 0.25 0.41
0.73 0.85 0.88
0.23 0.69 0.51
[5], different capital cost estimation methods are employed by different organizations. Furthermore, cost elements included in the capital cost also vary by the organization conducting the assessment. Fig. 3 shows the total capital cost requirement (TCR) of a supercritical PC-CCS plant as estimated by three organizations (namely EPRI [26], NETL [28], and Worley Parsons [32]). We convert the reported capital costs to 2010 US$ using the Chemical Engineering Plant Cost Index (CEPCI) [37]. Fig. 3 also shows different categories of costs that are included in the TCR. Of the categories presented, total overnight cost (TOC) includes the cost of all process equipment, material and labor, support facilities, engineering procurement services, etc. The ‘‘contingencies” category includes miscellaneous capital costs that are expected to be incurred as the plant construction phase progresses
Fig. 2. Variability of different parameters and their influence on CACO2. Technologies are color coded – blue is pulverized coal (PC), red is coal-fired integrated gasification combined cycle (IGCC) and green is natural gas combined cycle (NGCC). (LCOE – levelized cost of electricity; CCF – capital charge factor). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
and IGCC technologies are less proven). Given that CCS in general bears nonnegligible technical risks, one would expect to see higher discount rates being applied to the CCS plants than their comparative non-CCS equivalents. Most studies report the same discount rate (a component of CCF) and this is problematic. Only three studies we reviewed (National Energy Technology Laboratory (NETL) [28], Electric Power Research Institute (EPRI) [26], and Worley Parsons [32]) have explicitly identified this and have incorporated risk into their representation of CCF in a more detailed way. Our regression analysis shows that in all technologies analyzed, the influence of the capital cost of the CCS power plant on CACO2 is significant. However, careful examination of capital costs reported in different studies reveal structural differences. As discussed in
Fig. 3. Elements of capital costs estimates of PC-CCS made by three institutions ([26], [28], and [32]). (TOC – total overnight cost; IDC – Interest paid during construction).
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[17]. The contingency component of the three cases varies on the order of 11–17% of TCR. Another example of sources of differences are related to ‘‘owner’s costs”, the specific items included in this category vary by organization. For example, estimates by EPRI [26] and NETL [28] have nearly identical TOC. However, the size of the owner’s cost category in the NETL estimate [28] is almost 4 times that of EPRI estimate [26]. One reason for this difference is that NETL [28] includes the cost of feasibility studies, surveys, permitting, financing, and legal fees in the owner’s cost, but EPRI [26] does not. Capital cost estimates presented in Fig. 3 are from the three most detailed studies currently available publicly. From the examination, we see that even the experienced, knowledgeable institutions with access to extensive datasets take different approaches, draw different boundaries etc. that lead to important differences in capital cost estimates. Most other studies make much more simplified assumptions that can lead to further discrepancies in cost estimates. While efforts have been made to standardize estimates across the industry [17], there is still work to be done.
4. Conclusions In terms of carbon emission reduction technologies and climate policies, variability in cost estimates is problematic if the sources of uncertainty stem from modeling assumptions rather than inherent uncertainty about technology performance. Our work suggests that this indeed is the case for CCS technologies and this can lead to inefficient policies and/or reluctance to adopt the technology (as has been seen in many jurisdictions worldwide). We quantify the variability and influence of key parameters pertaining to three major CCS technologies using previous studies. We find that the parameters that have large variability are LCOE penalty, capital cost of CCS, fuel price, and efficiency penalty. However, fuel price is not influential in estimates of CACO2. Efficiency and LCOE penalties are not only variable but also have a significant influence on CACO2 estimates whereas fuel price is variable but not influential. The variability and magnitude of efficiency and LCOE penalty are both highly dependent upon the choice of baseline plant. The capital costs of a CCS power plant are also highly variable and influential on CACO2. However, the elements of cost components aggregated to calculate the capital cost vary considerably across studies and in some cases are not transparent. In terms of capital cost estimates made by different institutions, we observe structural differences in assumptions pertaining to boundaries (cost components that are included or excluded), project financing assumptions, and level of risk aversion. Details of elements of capital costs, which can be very different from jurisdiction to jurisdiction and during different economic climates, should be more explicitly included and articulated in future studies. As such, it is important that future CCS assessments are carefully structured and key assumptions are explicitly reported, making the interpretation and comparison of results less ambiguous. The choice of baseline impacts the results of CCS cost assessments. However, selection of a baseline is inevitable as policy and investment decisions require comparison of competing technologies. Therefore, characterization of a baseline should take into account the context in which the assessment is performed. Reflection of risk in the discount rate is important but doesn’t appear to be captured well across the studies we examined. These improvements would help to ensure that the remaining variability present reflects true uncertainty about the technology. This analysis provides more a systematic assessment of the role that different parameters play in explaining variability in cost estimates. The insights derived can also help to direct pilot and
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demonstration scale testing in order to better ascertain key aspects of performance that are currently highly uncertain. It can also help to identify opportunities to reduce the impacts of the uncertainty of key parameters and improve the comparability across CCS technology assessments. Finally, it can also help to reduce variability introduced due to modeling assumptions and potentially biased judgments related to the cost of CCS technologies. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apenergy.2015. 08.056. References [1] Bergerson J, Lave L. Baseload coal investment decisions under uncertain carbon legislation. Environ Sci Technol 2007;41:3431–6. [2] EIA. Electricity Data Browser. U.S. Energy Information Administration; 2014 [cited November 2014]. Available from: http://www.eia.gov/electricity/. [3] Li B, Duan Y, Luebke D, Morreale B. Advances in CO2 capture technology: a patent review. Appl Energy 2013;102:1439–47. [4] Herzog HJ. Scaling up carbon dioxide capture and storage: from megatons to gigatons. Energy Econ 2011;33:597–604. [5] Rubin ES. Understanding the pitfalls of CCS cost estimates. Int J Greenhouse Gas Control 2012;10:181–90. [6] Bergerson JA, Keith DW. The truth about dirty oil: is CCS the answer? Environ Sci Technol 2010;44:6010–5. [7] The Global Status of CCS: 2014. Global Carbon Capture and Storage Institute; 2014 [cited July 2015]. Available from: http://www.globalccsinstitute.com/ publications/global-status-ccs-2014. [8] E3G. Carbon Capture and Storage in China. Germanwatch e. V.; 2009 [cited July 2015]. Available from: http://germanwatch.org/klima/ccs-china.pdf. [9] Senior B, Chen W, Gibbins J, Haydock H, Li M, Pearce J, et al. Carbon capture and storage in China — main findings from China–UK Near Zero Emissions Coal (NZEC) initiative. Energy Proc 2011;4:5956–65. [10] Abellera C, Short C. The costs of CCS and other low-carbon technologies. Global Carbon Capture and Storage Institute; 2011 [cited July 2015]. Available from: http://www.globalccsinstitute.com/publications/costs-ccs-and-otherlow-carbon-technologies. [11] Schreiber A, Zapp P, Marx J. Meta-analysis of life cycle assessment studies on electricity generation with carbon capture and storage. J Ind Ecol 2012;16. S155-S68. [12] Finkenrath M. Cost and performance of carbon dioxide capture from power generation. International Energy Agency; 2011 [cited November 2014]. Available from: http://www.iea.org/publications/freepublications/ publication/costperf_ccs_powergen-1.pdf. [13] Jones F. UKERC technology and policy assessment cost methodologies project: CCS Case Study UK Energy Research Centre; 2012 [cited July 2015]. Available from: http://www.ukerc.ac.uk/asset/E108A88C-0741-44DB93CB079B6002DCE4/. [14] Allinson WG, Ho MT, Neal PR, Wiley DE. The methodology used for estimating the costs of CCS. In: 8th International Conference on Greenhouse Gas Control Technologies Trondheim, Norway. [15] Nemet GF, Baker E, Jenni KE. Modeling the future costs of carbon capture using experts’ elicited probabilities under policy scenarios. Energy 2013. [16] ICO2N. Perspectives on conducting cost analyzes of CO2 capture technologies report integrated CO2 network; 2007 [cited July 2015]. Available from: http:// www.ico2n.com/about/ico2n-research/perspective-on-capture-cost-analyses. [17] Rubin ES, Booras G, Davison J, Ekstrom C, Matuszewski M, McCoy S, et al. Toward a common method of cost estimation for CO2 capture and storage at fossil fuel power plants. Electric Power Research Institute; 2013 [cited November 2014]. Available from: http://www.epri.com/abstracts/Pages/ ProductAbstract.aspx?ProductId=000000003002000176. [18] Fischer C, Morgenstern RD. Carbon abatement costs: why the wide range of estimates? Energy J 2006;27:73–86. [19] Kuik O, Brander L, Tol RSJ. Marginal abatement costs of greenhouse gas emissions: a meta-analysis. Energy Policy 2009;37:1395–403. [20] O’Rourke K. A historical perspective on meta-analysis: dealing quantitatively with varying study results. J R Soc Med 2007;100:579–82. [21] Bergerson J, Lave L. The long-term life cycle private and external costs of high coal usage in the US. Energy Policy 2007;35:6225–34. [22] Black & Veatch. Cost and performance data for power generation technologies. Black & Veatch (prepared for National Renewable Energy Laboratory); 2012 [cited November 2014]. Available from: http://bv.com/docs/reports-studies/ nrel-cost-report.pdf. [23] CAESR. European best practice guidelines for assessment of CO2 capture technologies. CAESAR Consortium; 2011 [cited November 2014]. Available from: http://www.gecos.polimi.it/research/EBTF_best_practice_guide.pdf. [24] Chen C, Rubin ES. CO2 control technology effects on IGCC plant performance and cost. Energy Policy 2009;37:915–24.
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[25] Davison J. Performance and costs of power plants with capture and storage of CO2. Energy 2007;32:1163–76. [26] EPRI. Updated Cost and Performance Estimates for Clean Coal Technologies Including CO2 Capture-2006. Electric Power Research Institute; 2006 [cited November 2014]. Available from: http://www.epri.com/abstracts/Pages/ ProductAbstract.aspx?ProductId=000000000001013355. [27] IEA. Projected Costs of Generating Electricity – 2010 Edition. Paris: OECD; 2010. [28] NETL. Cost and Performance Baseline for Fossil Energy Plants Volume 1: Bituminous Coal and Natural Gas to Electricity. National Energy Technology Laboratory; 2010 [cited Available from: http://www.netl.doe.gov/research/ energy-analysis/energy-baseline-studies. [29] Rubin ES, Chen C, Rao AB. Cost and performance of fossil fuel power plants with CO2 capture and storage. Energy Policy 2007;35:4444–54. [30] Rubin ES, Yeh S, Antes M, Berkenpas M, Davison J. Use of experience curves to estimate the future cost of power plants with CO2 capture. Int J Greenhouse Gas Control 2007;1:188–97.
[31] Rubin ES, Zhai H. The cost of carbon capture and storage for natural gas combined cycle power plants. Environ Sci Technol 2012;46:3076–84. [32] Worley Parsons. Economic Assessment of Carbon Capture and Storage Technologies: 2011 Update. Worley Parsons and Global CCS Institute; 2011 [cited November 2014]. Available from: http://decarboni.se/sites/default/ files/publications/12786/economic-assessment-carbon-capture-and-storagetechnologies-2011-update.pdf. [33] ZEP. The Costs of CO2 Capture, Transport and Storage. European Technology Platform for Zero Emission Fossil Fuel Power Plants; 2011 [cited November 2014]. Available from: http://www.zeroemissionsplatform.eu/library/ publication/165-zep-cost-report-summary.html. [34] Goto K, Yogo K, Higashii T. A review of efficiency penalty in a coal-fired power plant with post-combustion CO2 capture. Appl Energy 2013;111:710–20. [35] Rayner JS, Best DJ. Smooth tests of goodness of fit. Oxford University Press; 1989. [36] Wakefield J. Bayesian and frequentist regression methods. Springer; 2013. [37] Economic Indicators. Chemical Engineering. 2014;121:80.
Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
A meta-analysis of carbon capture and storage technology assessments: Understanding the driving factors of variability in cost estimates Oguz Akbilgic a,b, Ganesh Doluweera b,1, Maryam Mahmoudkhani c,2, Joule Bergerson b,d,⇑ a
UTHSC-ORNL Center for Biomedical Informatics, 910 Madison Ave., Memphis, TN 38163, USA Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada c Department of Energy and Environment, Chalmers University of Technology, Sweden d Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada b
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
Meta-analysis to explain
discrepancies in previous studies assessing CCS costs. Regression models have strong predictive power (R2 > 0.90). Capital cost & relative thermal efficiency penalty have largest impact on cost. First analysis to quantify the contribution of parameters to variability of CCS cost. Analysis can be used to make future cost studies more transparent and comparable.
a r t i c l e
i n f o
Article history: Received 6 May 2015 Received in revised form 13 August 2015 Accepted 15 August 2015 Available online 8 September 2015 Keywords: Carbon capture and storage Meta-analysis Cost of carbon abatement Power plant cost estimation Regression analysis
a b s t r a c t The estimated cost of reducing carbon emissions through the deployment of carbon capture and storage (CCS) in power systems vary by a factor of five or more across studies published over the past 8 years. The objective of this paper is to understand the contribution of techno-economic variables and modeling assumptions to explain the large variability in the published international literature on cost of avoided CO2 (CACO2) using statistical methods. We carry out a meta-analysis of the variations in reported CACO2 for coal and natural gas power plants with CCS. We use regression and correlation analysis to explain the variation in reported CACO2. The regression models built in our analysis have strong predictive power (R2 > 0.90) for all power plant types. We find that the parameters that have high variability and large influence on the value of CACO2 estimated are levelized cost of electricity (LCOE) penalty, capital cost of CCS, and efficiency penalty. In addition, the selection of baseline technologies and more attention and transparency around the calculation of capital costs will reduce the variability across studies to better reflect technology uncertainty and improve comparability across studies. Ó 2015 Elsevier Ltd. All rights reserved.
⇑ Corresponding author at: Department of Chemical and Petroleum Engineering, University of Calgary, EEEL Building, Room 461B, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada. Tel.: +1 403 220 5265; fax: +1 403 220 2400. E-mail address: [email protected] (J. Bergerson). 1 Current address: Canadian Energy Research Institute, #150, 3512 - 33 Street NW, Calgary, AB T2L 2A6, Canada. 2 Current address: ConocoPhillips, Canada. http://dx.doi.org/10.1016/j.apenergy.2015.08.056 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
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1. Introduction In many regions globally, a coal-fired power generation plant produces the cheapest reliable and dispatchable electricity [1]. However, these plants are being challenged by concerns about conventional air pollutants and greenhouse gas emissions (GHG). This has lead to a decrease in their use over the past 5 years. For example, in the U.S., coal-fired electricity production decreased by 20% from 2000 TWh in 2008 to 1600 TWh in 2013 [2]. One technological option that can enable continued use of coal-fired plants in a carbon constrained economy is carbon capture and storage (CCS) [3,4]. However, the estimated cost of reducing carbon emissions through the deployment of CCS in power generating units is prohibitively high in many jurisdictions. To address the challenge of prohibitively high costs, advanced capture technologies are being investigated that could greatly reduce the cost of capture. Another challenge is that the magnitude of the costs estimated (even for existing technologies) can vary by a factor of five or more. This variability undermines support for policies to curb carbon emissions through deployment of CCS. That is, private energy investors tend to be reluctant to adopt a new technology without an understanding of its true costs. Therefore, this must be addressed prior to further establishment of policies or technology development directives. The variability in costs is often interpreted as the inherent uncertainty in the technology. In fact, it is likely that much of the variability across studies is due more to the modeling assumptions and boundaries of analysis than the technical uncertainty of how the technology will perform once deployed at commercial scale [5,6]. Variability in CCS cost and performance estimates has been studied previously with four main categories of literature discussed below. The first category includes studies that review CCS costs and trends as part of status updates on the technologies (e.g., [7]) and potential applications of CCS in specific geographic regions (e.g., [8,9]). The second category includes studies that review CCS costs as the basis of comparison of competitiveness against other low-carbon technologies (e.g., [10]). These two categories of studies demonstrate the need for consistency in conducting cost studies. The third category includes studies that attempt to understand the magnitude and impact of different assumptions and methods related to CCS costing. Schreiber et al. [11] conducted a metaanalysis of 15 environmental life cycle assessments of CCS technologies. However, they were unable to draw robust conclusions about the magnitude of the environmental impacts due to the variability in parameters and study boundaries used across the studies [11]. A comprehensive review of CCS costs and performance parameters has been made by Li et al. in [12], revealing wide ranges of important parameters. These include capital costs and plant efficiency penalty due to the capture process. Another review by Rubin developed ranges of cost estimates for different CCS technologies. This study revealed that significant differences exist in CCS cost estimation methods employed by leading government and industry organizations [5]. These differences are not only parametric such as the variability in key CCS technical, economic and financial assumptions but also structural such as the choice of metrics to estimate CO2 emissions abatement cost and the elements of cost that are included or excluded [5]. Jones [13] evaluates key trends in CCS cost estimates over time. While one of the objectives of the study was to ‘‘understand the drivers underlying the key trends”, this was done in a qualitative fashion. Allinson et al. [14] reviewed several methods used to estimate costs of CCS and highlighted the differences that can lead to variability. They also conducted a basic sensitivity analysis by varying a subset of parameters one at a time. They acknowledge that the combined sensitivities are important but they didn’t quantify these impacts. In [15], Nemet et al. employ expert elicitation and Monte-Carlo
simulation to construct probability distributions of carbon abatement costs for different CCS technologies. A wide range of CCS cost and performance estimates utilized for the Monte-Carlo simulation was found. These are due to differences in expert opinions and variability in CCS technology parameters found in published literature. The fourth category includes studies that make recommendations and propose guidelines for how to perform cost estimates of CCS. For example, ICO2N [16] described a high-level set of guidelines related to the types of parameters to include, the boundary of analysis etc. Rubin et al. [17] outlined a very thorough and extensive set of guidelines on methods that can be used to harmonize both CCS cost estimates as well as their communication to different audiences. These guidelines are informed by extensive experience in conducting cost estimates and reviewing other CCS cost studies. However, the paper does not quantify the relative contributions of the individual parameters to explain the discrepancies in CCS costs in a quantitative manner. The aforementioned studies provide evidence of the variability of CCS costs and performance estimates. However, none of these studies takes a quantitative approach to assessing the relative contribution of each parameter in explaining the variability seen across CCS cost studies. In this paper, we develop an analysis to understand and quantify the nature of the variability and the influence of relevant parameters on CCS cost estimates. This is the first study of its kind that deploys a modeling approach that explains the variability in cost estimates by incorporating the variability in the input parameters simultaneously. This analysis results in a better understanding of the source, nature, and magnitude of this variability to distinguish between what is due to operating and modeling decisions compared with inherent uncertainty about the technology itself. This can then be used to support and better inform the priorities around the parameters that should be included and communicated in future CCS cost studies. This analysis can, therefore, be used to help reduce variability due to modeling details in CCS cost estimates by quantitatively assessing the main driving factors of this variability. In addition, by understanding the source of the variability, we develop a better understanding of how to reduce this variability in future studies and make better comparisons across studies. We conduct a meta-analysis and regression analysis to understand the underlying relationships between parameters used to estimate the cost of CCS. Meta-analysis is a group of statistical techniques for combining and integrating the quantitative results from several independent studies. This can be used to explain the differences between studies and a more broadly based estimate of the existence, size and reliability of relevant effects. Metaanalysis, therefore, extends beyond a standard literature review by analyzing and synthesizing the results of multiple studies in a statistical manner [18–20]. We collect a set of observations consisting of input parameters and results of different studies that estimate the cost of CCS. We then use exploratory statistical analysis to explain variability in CCS cost estimates in terms of the model parameters most commonly used.
2. Methods Our meta-analysis includes a review of over 40 technical reports and articles. These studies report cost estimates of coal- and natural gas-fired power plants in North America and OECD Europe. Most have been published by leading government, industrial, and academic institutions involved in developing and/ or deploying CCS technologies. The studies were selected based on their scope and availability of all data required for the meta-analysis.
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In this paper, we focus on three types of power plants with CCS; namely, Pulverized Coal (PC), coal-fired Integrated Gasification Combined Cycle (IGCC) and Natural Gas Combined Cycle (NGCC). We collect the technology cost and performance data for the analysis from 15 studies that report all data used in the analysis (57 sets of observations: 21 for PC; 20 for IGCC; and 16 for NGCC) [1,12,21–33]. For the full dataset used for this analysis, see Tables S1–S3 of supplementary information (SI). Of the 21 observations pertaining to PC power plants, the technologies considerations are: 18 supercritical pulverized coal power plants; 2 ultra- supercritical pulverized coal power plants; and 1 sub-critical pulverized coal power plant. There are many ways to express the cost of avoiding CO2 emissions from CCS applications. These include the cost of CO2 abated (CACO2) and cost of CO2 captured [5]. We select CACO2 for our analysis because it is the most commonly reported cost of reducing CO2 using CCS. Furthermore, CACO2 captures CO2 associated with both the net output energy and parasitic energy consumption of the capture process. As such, comparisons based on CACO2 incorporate the benefits of efficient capture technologies. CACO2 also allows for a more direct comparison with other CO2 mitigation technologies. CACO2 compares a plant with CCS to a baseline (or reference) plant and calculates the average cost of avoiding emissions of a unit mass of CO2 (typically measured in $ per metric ton of CO2 avoided) [5]. By definition, baselines must be identified to determine CACO2 of the CCS power plant under study. Defining the baseline is difficult as there’s no clear ‘best answer’ [1,5]. A CCS project should be compared to a system producing an equal quantity of the same product (e.g., electricity) to what would be built in the absence of the CCS plant. Most often, several options exist for producing any given product. For example, power can be produced using fossil-based options such as PC, IGCC, and NGCC technologies. However, it can also be produced using low carbon options such as nuclear or renewable energy technologies. Therefore, any one of these options could be considered the baseline for a new power plant with CCS. The baseline should be determined for a specific jurisdiction based on the technology that is most likely to be built in the absence of CCS technologies (e.g., resource availability, incentives such as Renewable Portfolio Standards). It is interesting to note that the baseline plant of each and every study we extracted data from is the plant under study without CO2 capture. This indicates that even though large variability exists, it might not capture the full variability nor the true costs of avoided CO2 in all applications/jurisdictions. For a given power plant with CCS, CACO2 is calculated by Eq. (1), where LCOE is the levelized cost of electricity in $/MWh. For our analysis, we calculated CACO2 using Eq. (1) for studies that did not explicitly report CACO2.
CACO2 ¼
LCOEPlant with CCS LCOEBaseline plant ðCO2 emissions=MWhÞBaseline plant ðCO2 emissions=MWhÞPlant with CCS ð1Þ
LCOE and CO2 emissions of a power plant are determined by its capital cost, fuel price, efficiency, annual utilization, CO2 content of the fuel, and financial assumptions such as the economic life of the project and the discount rate. Therefore, in estimating CACO2, there are many variables that affect the estimate of cost. When selecting the relevant variables for our analysis we hypothesize that the following parameters have the largest influence on the CACO2 of CCS technologies. In general, capital cost represents the largest cost associated with a power plant. The influence of capital cost is represented in our analysis by capital cost of CCS (CCccs), capacity factor (CF), and capital charge factor (CCF). Fuel cost, in general, is the second largest cost and it is represented
13
by fuel (coal and natural gas) price (FP) and thermal efficiency of the CCS power plant (Effccs). Project economic life and discount rate are collectively represented by the capital charge factor (CCF). Since CACO2 is measured with reference to a baseline, it is important that we incorporate the influence of the baseline plant. LCOEpen is the relative increase in LCOE of the CCS power plant compared to its baseline power plant. Similarly, the efficiency penalty (Effpen) is the relative decrease in CCS power plant efficiency due to power consumed by the CO2 capture and compression units compared to its baseline [34]. The latter two parameters represent the influence of the baseline power plant on CACO2. In addition to their influence on the costs of CCS, Effccs and Effpen also determine the CO2 emissions associated with the CCS power plant (i.e., they influence both the numerator and denominator of Eq. (1)). Therefore, they may have a larger influence on CACO2. Furthermore, reduction in power plant efficiency resulting from the CO2 capture process is touted to be a major barrier for CCS technologies [34]. CCS cost estimates in the studies we include in our analysis have been made over different time periods and for different jurisdictions. Therefore, the year and the jurisdiction of the estimate are two additional parameters that may influence CACO2. However, we do not investigate the influence of the jurisdiction on CACO2 due to insufficient data from different jurisdictions. The majority of the cost data we collected are for the U.S. and European Organisation for Economic Co-operation and Development countries. Therefore, our insights are directed to these jurisdictions. Furthermore, in our analysis, CACO2 is considered a variable that is a linear combination of the other variables. Thus, the year of the estimate is indirectly taken into account via the other variables considered. Finally, we did test the influence of these variables in our regression model using stepwise linear regression (see Table S8 of SI). Neither variable was selected in the model, which means they don’t significantly contribute to the explanation of variability in the dependent variable. 2.1. Assessment of parametric variability The variables considered in our analysis are populated with the input values in the original models or calculated based on the primary parameters presented in the articles/reports. For one study [22] we calculate the cost of electricity (assuming a discount rate of 10% and project economic life of 30 years) and CACO2. We start our analysis by visually assessing the variability of different variables using box plots. We calculate descriptive statistics such as min, median, mean, max, and standard deviation. We use the coefficient of variation (the ratio between the standard deviation and the mean of a given variable) as a measure of variability of each variable. Since the variables we use have different units, a normalized measure such as coefficient of variation allows us to directly compare the relative variability of respective variables. We also calculate correlation coefficients between CACO2 and each independent variable. 2.2. Assessment of the impact of different variables on CACO2 To investigate what drives the differences between CCS cost estimates in the literature, we employ correlation analysis. Correlation analysis is one of many methods that can be used to investigate the nature and strength of the relationship between two or more variables. In order to conduct a correlation analysis, first we build a regression model [35]. In regression, the statistical relationship between independent variables and the dependent variable is assumed to be linear [35,36]. Therefore, a linear regression model is simple and easy to interpret. When linearity is satisfied, the parameters of the linear regression model expose the individual influence of each independent variable. In this paper,
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we build a multiple linear regression (MLR) model to study the statistical relationship between CACO2 and the other model variables. Our goal here is not only to predict CACO2 but also to understand which model variables explain the most variation in CACO2. There are many ways to assess the goodness of fit of a statistical model (i.e., how well a model fits data and the validity of a model [35]). We use the R2 statistic to measure how well our linear model estimates CACO2 using a linear combination of the other variables as inputs. Although we are aware of the non-linear nature of the relationship between CACO2 and several of the variables considered in our analysis, we assume that the non-linear part of the relationships are negligible. Therefore, they can be excluded from our model. The goodness of fit of the models, R2, is then used to decide if the linear regression model is sufficient and confirm that the nonlinear terms can be neglected. We also use variable selection techniques to determine the most significant predictors within our models. We use stepwise regression (systematically adding and removing independent variables) to create several models and test their relative performance using the F-test and t-test [36]. The MLR model we build is given in Eq. (2).
CACO2 ¼ b0 þ b1 FP þ b2 CCF þ b3 CF þ b4 CCCCS þ b5 Eff CCS þ b6 LCOEpen þ b7 Eff pen þ e
ð2Þ
In Eq. (2), b0 b7 are the regression coefficients and e Nð0; r2 Þ is the random error term. We estimate b0 b7 and r (standard deviation) by utilizing ordinary least squares based regression model fitting techniques [36]. When independent variables are measured in different units, standardized regression coefficients (standard beta coefficients) are needed to compare the relative importance of independent variables. Standard beta coefficients are calculated by first standardizing the independent variables so that their variances are unity, and then by fitting the MLR model given in Eq. (2) using standardized independent variables. In our problem, standard regression coefficients show the variation in CACO2 corresponding to one standard deviation increment of the corresponding independent variable. 2.3. Synthesizing variability analysis and influence analysis Finally, to derive insights to inform future CCS cost assessments, we combine the results of the parametric variability analysis and the assessment of the impact of different variables through MLR modeling. We examine the calculated coefficient of variation and standard beta coefficients to identify variables that have a large overall impact on CCS costs. That is, higher variability and higher impact on CACO2. We also examine the studies we reviewed for structural variations in methods employed for CCS technology assessments. 3. Results and discussion 3.1. Parametric variability and correlation The data extracted from 15 studies are summarized in Fig. 1 by technology type for six independent variables and one dependent variable (CACO2). Descriptive statistics associated with this data are presented in Table 1. The table also summarizes the degree of variability (Coefficient of variation) and the degree of correlation between each input parameter and CACO2 (correlation with CACO2). The spread in CACO2 across observations for PC coal power plants is large; the minimum and maximum estimates are 34 and 112 $/tonne of CO2 respectively. We observe the same trend for the other two types of plants, IGCC and NGCC, (i.e. a minimum
of $20 and maximum of 114 $/tonne of CO2 for IGCC plants, and a minimum of 45 and maximum of 224 $/tonne of CO2 for NGCC plants). The calculated coefficient of variation (i.e., standard deviation as a percentage of the mean) of CACO2 for PC, IGCC, and NGCC plants are 34%, 52%, and 51% respectively. It is interesting to note that while the variability in the estimate of CACO2 is high for all three technologies, it is lowest for PC plants. While the variation of FP is high for all plant types, it is higher for coal, which is surprising. This is likely due to regional variations of coal prices compared to natural gas, which is generally more consistent across regions but is more volatile. The correlation coefficients between CACO2 and each independent variable are calculated and reported in Table 1. The correlation coefficient for each variable indicates that CACO2 has a strong correlation (i.e., correlation coefficient greater than 0.6) with two independent variables: LCOEpen and CCccs for all three power plant types. However, for IGCC power plants, the Effpen variable is also strongly correlated with CACO2. This is likely due to the fact that the Effpen impacts both the numerator (LCOE differential) and the denominator (emissions differential) of Eq. (1), which is used to calculate CACO2. Effpen might be significant for IGCC plants and not for the other two technologies because the relative capital cost difference between the IGCC plant with and without CCS is smaller than that for PC and NGCC plants. Consequently, the difference in fuel consumption between the IGCC plant with and without CCS is larger and has a higher impact on CACO2 than that for the other two technologies. For all plant types, the correlations of other variables with CACO2 are not significant. 3.2. Impact of independent variables on CACO2 Here, we analyze the statistical relationship between the cost of avoided CO2 (CACO2) and seven independent variables using linear regression analysis. We carry out our analysis separately for each power plant. The regression coefficients derived from the analysis are presented in Table S6 of the SI. Note that all three regression models have very strong explanatory power (PC R2 = 0.90; IGCC R2 = 0.93; NGCC R2 = 0.98). That is, according to the regression models, the variability in the cost of captured CO2 is very well represented with the linear models using the variables considered in this study. This also confirms that the nonlinear effects are not required since the linear models explain more than 90% of the variability. Although all three regression models are significant, several of the regression coefficients are not. In other words, some of the independent variables do not make a significant contribution to the explanation of the variation in CACO2 in the linear regression model. Stepwise regression techniques expose the key determinants of the cost of avoided CO2 (See Section S3 & Table S7 of SI). This analysis shows that the subset of independent variables that best explains the variability in the CACO2 includes CCccs, LCOEpen, and Effpen for all plant types (i.e., are significant variables). Since these three variables are the only variables in the stepwise regression models for IGCC and NGCC plants, we can conclude that IGCC and NGCC power plants are similar in terms of the determinants of CACO2. However, in addition to CCccs, LCOEpen, and Effpen, CF also plays an important role in PC power plants. This is confirmed by comparing the standard betas of each variable for each power plant type (see the SI for details). Standard betas also quantify the magnitude of the impact of each important variable. LCOE is another important parameter to estimate CACO2. However, LCOE is not directly included as a predictor in the regression analysis since it can be explained by the other variables that are already in the model (See Section S2 of SI for further support of this hypothesis).
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Fig. 1. Distributions of avoided cost of CO2 estimates as well as variables that affect CACO2 across studies for three types of CCS technologies. Data points are marked as ‘s’. For a given data set, the bottom and top edges of the boxes correspond to the 25th and 75th percentile respectively. The solid horizontal line inside the box corresponds to the median. The upper whiskers extend to the maximum data point value that is within 1.5 times the interquartile range from the top edge of the box. The same is true for the lower whiskers. Outlier data points are marked as ‘X’. Colors represent different technologies: Blue is pulverized coal (PC), red is coal-fired integrated gasification combined cycle (IGCC) and green is natural gas combined cycle (NGCC). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.3. Synthesizing variability analysis and influence analysis In Fig. 2 we examine the magnitude of variability and the level of influence of the parameter in explaining the CACO2 simultaneously. The LCOE penalty and efficiency penalty are both highly variable and have a large influence on CACO2 (both have large coefficients of variation and standard beta values) for all three technologies. This reaffirms the importance of careful selection of a baseline for CACO2 estimates. This is due to the fact that these parameters are calculated by comparing the performance of a plant with CCS to a baseline plant that would be built in the absence of the CCS option. The baseline plant is a structural choice made by
the developer of the CCS cost estimation model and, therefore, has an impact on the variability and magnitude of CACO2. When selecting a baseline there is no ‘best answer’ and the choice should be made taking into account the context within which the CACO2 estimate is made. CCF is often touted to be highly influential in determining avoided cost. Surprisingly, it is neither highly variable nor highly influential across the studies investigated. It is also interesting to note that the influence of CCF is lower for IGCC than it is for PC or NGCC plants. This is in spite of the fact that the technological risks associated with IGCC generally tend to be considered higher (i.e., CCF is often used to represent the level of technological risk
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Table 1 Descriptive statistics and correlations of model variables. Technologies included are pulverized coal (PC), coal-fired integrated gasification combined cycle (IGCC) and natural gas combined cycle (NGCC). Statistics included are capital cost of CCS (CCccs), capacity factor (CF), capital charge factor (CCF), fuel (coal and natural gas) price (FP), thermal efficiency of the CCS power plant (Effccs), capital charge factor (CCF), the relative increase in levelized cost of electricity of the CCS power plant compared to its baseline power plant (LCOEpen) and the relative decrease in CCS power plant efficiency due to power consumed by the CO2 capture and compression units compared to its baseline (Effpen Numbers in bold indicate strong correlations.). Statistics
Plant type
FP ($/GJ)
CCF (/year)
CF
CCccs ($/kW)
Effccs
LCOEpen (%)
Effpen (%)
CACO2 ($/tCO2)
Min
PC IGCC NGCC
1.0 1.0 3.3
0.09 0.09 0.09
0.75 0.75 0.75
1838 1815 869
0.26 0.29 0.34
40 24 28
15 12 14
34 20 45
Median
PC IGCC NGCC
2.0 2.1 7.3
0.10 0.11 0.10
0.85 0.85 0.85
3404 3143 1431
0.30 0.31 0.43
60 36 33
25 20 15
59 43 75
Mean
PC IGCC NGCC
2.2 2.2 7.3
0.11 0.11 0.11
0.83 0.83 0.82
3288 3292 1637
0.30 0.32 0.42
68 43 40
25 19 17
61 46 90
Max
PC IGCC NGCC
4.2 4.2 9.5
0.15 0.15 0.15
0.86 0.86 0.86
6560 6600 3750
0.36 0.36 0.45
130 131 98
36 26 34
112 114 224
Standard deviation
PC IGCC NGCC
0.9 0.9 1.9
0.02 0.02 0.02
0.04 0.04 0.05
1155 1289 738
0.03 0.02 0.03
23 23 17
4.4 3.7 6.0
20.5 23.8 45.6
Coefficient of variation
PC IGCC NGCC
38 40 26
17 16 16
5 5 6
35 39 45
9 6 8
34 54 44
18 19 34
34 52 51
Correlation with CACO2
PC IGCC NGCC
0.3 0.4 0.3
0.34 0.40 0.25
0.29 0.46 0.49
0.73 0.66 0.93
0.19 0.25 0.41
0.73 0.85 0.88
0.23 0.69 0.51
[5], different capital cost estimation methods are employed by different organizations. Furthermore, cost elements included in the capital cost also vary by the organization conducting the assessment. Fig. 3 shows the total capital cost requirement (TCR) of a supercritical PC-CCS plant as estimated by three organizations (namely EPRI [26], NETL [28], and Worley Parsons [32]). We convert the reported capital costs to 2010 US$ using the Chemical Engineering Plant Cost Index (CEPCI) [37]. Fig. 3 also shows different categories of costs that are included in the TCR. Of the categories presented, total overnight cost (TOC) includes the cost of all process equipment, material and labor, support facilities, engineering procurement services, etc. The ‘‘contingencies” category includes miscellaneous capital costs that are expected to be incurred as the plant construction phase progresses
Fig. 2. Variability of different parameters and their influence on CACO2. Technologies are color coded – blue is pulverized coal (PC), red is coal-fired integrated gasification combined cycle (IGCC) and green is natural gas combined cycle (NGCC). (LCOE – levelized cost of electricity; CCF – capital charge factor). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
and IGCC technologies are less proven). Given that CCS in general bears nonnegligible technical risks, one would expect to see higher discount rates being applied to the CCS plants than their comparative non-CCS equivalents. Most studies report the same discount rate (a component of CCF) and this is problematic. Only three studies we reviewed (National Energy Technology Laboratory (NETL) [28], Electric Power Research Institute (EPRI) [26], and Worley Parsons [32]) have explicitly identified this and have incorporated risk into their representation of CCF in a more detailed way. Our regression analysis shows that in all technologies analyzed, the influence of the capital cost of the CCS power plant on CACO2 is significant. However, careful examination of capital costs reported in different studies reveal structural differences. As discussed in
Fig. 3. Elements of capital costs estimates of PC-CCS made by three institutions ([26], [28], and [32]). (TOC – total overnight cost; IDC – Interest paid during construction).
O. Akbilgic et al. / Applied Energy 159 (2015) 11–18
[17]. The contingency component of the three cases varies on the order of 11–17% of TCR. Another example of sources of differences are related to ‘‘owner’s costs”, the specific items included in this category vary by organization. For example, estimates by EPRI [26] and NETL [28] have nearly identical TOC. However, the size of the owner’s cost category in the NETL estimate [28] is almost 4 times that of EPRI estimate [26]. One reason for this difference is that NETL [28] includes the cost of feasibility studies, surveys, permitting, financing, and legal fees in the owner’s cost, but EPRI [26] does not. Capital cost estimates presented in Fig. 3 are from the three most detailed studies currently available publicly. From the examination, we see that even the experienced, knowledgeable institutions with access to extensive datasets take different approaches, draw different boundaries etc. that lead to important differences in capital cost estimates. Most other studies make much more simplified assumptions that can lead to further discrepancies in cost estimates. While efforts have been made to standardize estimates across the industry [17], there is still work to be done.
4. Conclusions In terms of carbon emission reduction technologies and climate policies, variability in cost estimates is problematic if the sources of uncertainty stem from modeling assumptions rather than inherent uncertainty about technology performance. Our work suggests that this indeed is the case for CCS technologies and this can lead to inefficient policies and/or reluctance to adopt the technology (as has been seen in many jurisdictions worldwide). We quantify the variability and influence of key parameters pertaining to three major CCS technologies using previous studies. We find that the parameters that have large variability are LCOE penalty, capital cost of CCS, fuel price, and efficiency penalty. However, fuel price is not influential in estimates of CACO2. Efficiency and LCOE penalties are not only variable but also have a significant influence on CACO2 estimates whereas fuel price is variable but not influential. The variability and magnitude of efficiency and LCOE penalty are both highly dependent upon the choice of baseline plant. The capital costs of a CCS power plant are also highly variable and influential on CACO2. However, the elements of cost components aggregated to calculate the capital cost vary considerably across studies and in some cases are not transparent. In terms of capital cost estimates made by different institutions, we observe structural differences in assumptions pertaining to boundaries (cost components that are included or excluded), project financing assumptions, and level of risk aversion. Details of elements of capital costs, which can be very different from jurisdiction to jurisdiction and during different economic climates, should be more explicitly included and articulated in future studies. As such, it is important that future CCS assessments are carefully structured and key assumptions are explicitly reported, making the interpretation and comparison of results less ambiguous. The choice of baseline impacts the results of CCS cost assessments. However, selection of a baseline is inevitable as policy and investment decisions require comparison of competing technologies. Therefore, characterization of a baseline should take into account the context in which the assessment is performed. Reflection of risk in the discount rate is important but doesn’t appear to be captured well across the studies we examined. These improvements would help to ensure that the remaining variability present reflects true uncertainty about the technology. This analysis provides more a systematic assessment of the role that different parameters play in explaining variability in cost estimates. The insights derived can also help to direct pilot and
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