markal and the role of ccs in greenhouse gas mitigation scenarios

8/9/2019 MARKAL and the Role of CCS in Greenhouse Gas Mitigation Scenarios

1/65

MARKAL and the Role of CCS inGreenhouse Gas Mitigation

Scenarios

Christopher Bennett, John Bistline,Joo Eun Lee, Paul Mobley

MS&E 294: Climate Policy AnalysisProfessor John Weyant

Stanford University

March 2010


2/65

Acknowledgements

We would like to thank John Weyant and Kenny Gillingham for their input and feedbackthroughout the duration of the project. Additionally, we want to express our gratitude to Karim

Farhat who served as a consultant for this project and wrote portions of the introductory materialfor this report. We also are grateful for the assistance and cooperation of Dan Loughlin and theEPA MARKAL team and also Jack Moore who assisted with the MERGE results.


3/65

Table of Contents

ACKNOWLEDGEMENTS ..........................................................................................................................2

TABLE OF CONTENTS ..............................................................................................................................3

EXECUTIVE SUMMARY ...........................................................................................................................5

1. INTRODUCTION .....................................................................................................................................6

1.1.MOTIVATIONS AND SCOPE ....................................................................................................................61.2.CCSBACKGROUND ..............................................................................................................................6

2. MODEL BACKGROUND .......................................................................................................................9

2.1.MARKAL ............................................................................................................................................92.2.EPAMARKALDATABASE................................................................................................................102.3.MODEL SCENARIO DEVELOPMENT......................................................................................................11

3. CCS MODEL REPRESENTATION ....................................................................................................13

3.1.CCSTECHNOLOGIES IN MARKAL.....................................................................................................133.2.MODEL INCORPORATION:RETROFITS .................................................................................................14

3.2.1. Background .................................................................................................................................143.2.2. Literature Review ........................................................................................................................153.2.3. Policy Implications .....................................................................................................................163.2.4. Model Incorporation ...................................................................................................................17

3.3.MODEL INCORPORATION:PARTIAL CAPTURE .....................................................................................183.3.1. Background .................................................................................................................................183.3.2. Literature Review ........................................................................................................................183.2.3. Policy Implications .....................................................................................................................203.2.4. Model Incorporation ...................................................................................................................20

3.4.MODEL INCORPORATION:STORAGE ....................................................................................................213.4.1. Background .................................................................................................................................213.4.2. Literature Review ........................................................................................................................22

3.4.3. Policy Implications .....................................................................................................................233.4.4. Model Incorporations .................................................................................................................24

4. RESULTS ................................................................................................................................................26

4.1.ORIGINALDATABASE RUNS................................................................................................................264.1.1. Business-as-Usual (BAU) ...........................................................................................................264.1.2. L1 Mitigation Scenario ...............................................................................................................28

4.2.ENHANCED CCSMODEL.....................................................................................................................304.3.TECHNOLOGICAL AND POLICY UNCERTAINTY CASES .........................................................................334.4.COST COMPARISONS ...........................................................................................................................37

5. DISCUSSION..........................................................................................................................................41

5.1.WIND DEPLOYMENT ...........................................................................................................................41

5.2.NUCLEAR DEPLOYMENT .....................................................................................................................425.3.CAPTURE READY DISCUSSION ............................................................................................................445.4.PARTIAL CAPTURE DISCUSSION ..........................................................................................................445.5.DEMAND-SIDE EFFECTS ......................................................................................................................47

6. CONCLUSIONS .....................................................................................................................................51

6.1.SUMMARY OF RESULTS.......................................................................................................................516.2.MODEL LIMITATIONS AND FUTURE WORK .........................................................................................52

BIBLIOGRAPHY........................................................................................................................................53


4/65

APPENDIX A: PARTIAL CAPTURE MODEL ......................................................................................55

APPENDIX B: PARTIAL CAPTURE SENSITIVITY ANALYSIS .......................................................59

APPENDIX C: OTHER MITIGATION SCENARIOS ANALYZED ....................................................61

APPENDIX D: SUMMARY OF MARKAL SCENARIOS .....................................................................65


5/65

Executive Summary

This research explores the role of carbon capture and sequestration (CCS) in mitigating climatechange under different policy and technological development scenarios. The main goal of the

research is to determine how policy affects the deployment of CCS and how CCS may affectpolicy. This analysis focuses on the supply side of the US electricity sector from 2000 to 2050.We examine policy scenarios that include binding emissions trajectory targets with the goal ofstabilizing long-term atmospheric carbon dioxide (CO2) concentrations.

The MARKet ALlocation (MARKAL) model is chosen for this investigation due to its detailedtechnological representation and ability to quantify system-wide effects. The model is used inconjunction with the 2008 EPA database of the US energy sector. The CCS structure in theexisting framework is expanded in four primary ways: limiting the capacity of retrofits to existingcoal plants, employing capture ready systems with retrofit options, including partial capture CCSalternatives, and replacing a fixed cost for storage and transportation with a storage supply curve.

This analysis shows that CCS will likely be a major mitigation option along with wind andnuclear electricity technologies in coming decades. The primary conclusions from this work are:

Major deployment of at least one of these low-carbon technologies is needed to meetstrict or even moderate caps on emissions, and CCS is often part of that mix.

Limited technology portfolios increase abatement costs. We considered policy andtechnology development uncertainty scenarios, such as the impact of limited deploymentof new nuclear and intermittent resources on CCS development. These scenarios suggestthat CCS will play a larger abatement role when these options are delayed or unavailable.

CCS deployment is largest when: 1. Near-term reduction goals require holdovertechnologies (e.g., delayed implementation of advanced low-carbon technologies); 2.Energy storage and grid integration technology development is delayed and limits wind

growth; 3. Political factors or cost escalation prevent new nuclear capacity; 4. Demand isless responsive to price increases.

Integrated Gasification Combined Cycle (IGCC) with CCS is the dominant near-termCCS technology, since it has been proven at a demonstration scale and is technologicallymore mature than other CCS configurations.

Enhanced oil recovery (EOR) may provide an opportunity to accelerate CCS deploymentwith a value-added opportunity from CO2 storage.

This analysis offers a strong, systems-level foundation for investigating the role of CCS inclimate policy scenarios. However, there are many effects not incorporated into the model thatmay impact future CCS development. Due to the perfect foresight structure of MARKAL, theeffect of uncertainty in energy systems planning is difficult to incorporate into the analysis.

Although partial capture and capture ready CCS configurations did not deploy in this analysis,these technologies will likely function as hedging strategies against policy and technologyuncertainties. MARKAL also assumes inelastic demand, but under strict CO2 mitigation policies,demand-side adjustments to energy price fluctuations will likely contribute significant emissionsreductions while offering lower compliance costs for abatement.

This analysis confirms that CCS is not a silver bullet technology. Nevertheless, since theelectricity sector plays a large role in reducing CO2 emissions within the economy, CCS plays anespecially important role as part of a diverse portfolio of mitigation technologies.


6/65

1. Introduction

1.1. Motivations and Scope

Amid the increasing scientific consensus and growing public chorus that global climate changepresents an urgent and significant problem, many governments have established greenhouse gas(GHG) emissions targets in an effort to reduce the negative environmental externalities that thesegases pose. The current global CO2 emissions associated with energy production andconsumption are 31,600 million metric tons (MMTCO2), with the US contributing 6,400MMTCO2 (almost 20 percent) of the overall emissions (British Petroleum, 2009). Nearly 60percent of these emissions come from stationary sources, particularly power plants andmanufacturing facilities. This characteristic allows CO2 emissions to be captured and potentiallysequestered in geologic formations instead of being released to the atmosphere. The family oftechnologies that uses such abatement is referred to as CCS. In the US, nearly a third of all CO2emissions come from energy transformations from coal resources, which are used to generateabout half of the countrys electricity. Taking into consideration its abundance and relative coststability, coal is expected to remain a major fuel for electricity generation plants at least in thenear-term time horizon. However, as the urgency of imposing constraints on CO2 emissionsgrows, integrating CCS technologies into the existing coal infrastructure becomes more likely.

While many prominent climate change abatement models project large contributions from CCS(Stern, 2006; IEA, 2007; Vattenfall/McKinsey, 2007), the wide range of mitigation estimatesreflects the inherent technological, economical, and geopolitical uncertainties that surround thedevelopment and deployment of this technology. Technological uncertainties center on theeffectiveness of the CO2 capture processes, the efficiency and reliability of CO2 capture units, theaccuracy of modeling, controlling, and monitoring CO2 storage underground, and thecomprehensiveness and understanding of the physical, chemical, and geological aspects of CO2interactions with the hosting subsurface. Although the US Department of Energy has committedto investing $4 billion to support of CCS deployment, no large-scale demonstration projects havebeen implemented, primarily due to the perceived high cost. Additionally, the spatial andtemporal extent of CCS projects and associated public concerns creates unaddressed regulatorydifficulties. In fact, policy uncertainties also include an unknown horizon for energy and climatechange legislation in the US, which carries with it many variables about the level of futurefunding for new technology development and the stringency of emissions restrictions. Therefore,scenario analysis using energy-economic models is important to determine the primary driversthat encourage and inhibit CCS as a player in abatement efforts.

Given this complex backdrop, the primary question that this paper poses is what role CCS canplay to mitigate climate change. Our objective is to study the effects of different policy scenarioson the development of CCS technologies in the context of the US electricity sector until 2050.This research will focus on two sequential areas of analysis. First, this paper introduces four

major improvements to the current representation of CCS technologies in the MARKAL energy-economic model, particularly within the EPAs MARKAL database. Second, this enhanced CCSrepresentation is used to examine the effect of various policies on development and deployment.

1.2. CCS Background

CCS is one of the major technologies currently being investigated to mitigate climate change.These technologies capture CO2 from major point sources, compress it, and transport it inpipelines to suitable geologic formations where it is intended to be stored permanently.


7/65

Currently, there are several proposals for capture configurations. The three technologies alreadyimplemented or under commercial development are post-combustion capture, pre-combustioncapture, and oxy-fuel combustion. Post-combustion capture involves scrubbing CO2 from the fluegas emitted by power plants or industrial facilities using amine-based chemical sorbents likemonoethanolamine (MEA) and methyldiethanolamine (MDEA)(Wall, 2007). This technology is

currently implemented in some large-scale industrial processes and is equivalent to traditionalsmoke stack controls for SO2 and NOx emissions. In the pre-combustion (typically involvingIGCC), syngas mixtures of H2 and CO are produced from fuel partial combustion. Shift reactorsconvert CO into a more concentrated CO2 stream, which is then captured prior to H2 combustion.Oxy-fuel combustion processes offer a variation to the post-combustion capture route and involveusing pure oxygen rather than air for combustion, which only requires water to be condensedfrom the flue gas to produce a neat CO2 stream. While other techniques for CO2 removal arecurrently under investigation, the three primary configurations listed above represent the mostmature technology options for integration in large-scale capture operations.

Each of the three major technologies above has its own set of technological uncertainties that maylimit deployment in the near-term. For post-combustion capture, NOx and SOx in the flue gas can

react with MEA to form stable salts, which decreases the CO2 absorption capacity. Similarly,although oxy-fuel combustion systems prove to release almost one-third less NOx than thoseusing air, few fundamental studies have been conducted to assess the systems operations, and nopractical scale applications have been implemented yet. For IGCC plants, there are many factorsthat make the design appealing: the ability to capture CO2 prior to dilution by combustion air, thehigh CO2 partial pressure in the syngas, and the flexibility of combustion system design.However, this configuration is still only about as efficient (on a lower heating value basis) as theother two options. Economically, the costs of the CO2 capture systems associated with post-combustion are mostly due to the large amount of solvent used and the energy needed toregenerate it. The air separation units form the major expense in IGCC and oxy-fuel capturesystems. Since the levelized cost of electricity appears to be lowest for IGCC systems with CCS,these indications make IGCC theoretically the most appealing coal-fired CCS technology (Wall,

2007). However, uncertainty regarding IGCCs delayed commercialization leaves room to arguethat amine-based absorption is the most feasible and robust technique to be currently applied forCO2 capture (Rachelle, 2009).

In 2008, the DOE published the second edition of the Carbon Sequestration Atlas of United Statesand Canada (DOE/NETL, 2009). This document presents a reasonably comprehensive overviewof the applicability of the CCS technology in the US and Canada, providing quantitative estimatesand detailed locations of the CO2 sources and storage sites. This document considers CO2 storagein five major geologic formations: depleted oil and gas reservoirs, saline formations, unmineablecoal seams, oil- and gas-rich organic shales, and basalt formations. Generally, only the first threeCO2 sinks are considered for practical large-scale CO2 sequestration.

These estimates of CO2 storage resources in deep saline aquifers assume: 1. the formations areheterogeneous and thus involve multi-phase fluid flow; 2. only 25-75 percent of the aquiferthickness and 20-80 percent of its area is available for storage; 3. the net-effective porosity,vertical displacement efficiency, gravity, and microscopic displacement efficiency are not idealand thus contribute to an overall storage efficiency factor that is not higher than 1-4 percent. Inaddition, only saline aquifers with a suitable seal and in which CO2 can exist in supercriticalphase are considered. The storage resources of CO2 in coal seams are estimated using similar setof assumptions. Finally, CO2 storage in depleted oil and gas reservoirs is estimated to be the total


8/65


9/65

2. Model Background

2.1. MARKAL

The MARKAL model is a bottom-up energy-economic systems model, driven by user-inputteddata. The model was originally developed by the Brookhaven National Laboratory in the 1970sand has since been developed by the Energy Technology Systems Analysis Program (ETSAP) atthe International Energy Agency (IEA). MARKAL is currently used in more than 40 countries forenergy planning and research (EPA, 2009). This analysis uses MARKAL (Noble-Soft Systems,2007; Seebregts, 2001) to investigate the role of CCS in climate change mitigation scenarios.

The basic linear programming (LP) MARKAL variant uses a partial equilibrium economicstructure in which supply equals demand only within the energy sector to calculate both volumes(through the primal solution) and prices (through the dual solution). MARKAL is typically usedfor analyses involving medium time horizons (typically around 50 years), since thetechnologically-detailed model cannot predict breakthrough technologies (e.g., nuclear fusion)whose parameters and deployment times are highly uncertain. One of the most importantassumptions of the model is perfect foresight of economic agents throughout the entire timehorizon. This assumption has significant implications for this analysis, since the model alwaysoptimizes over the entire model period with complete knowledge of magnitudes and timing ofpolicies and technologies.

MARKALs economic-optimization model runs are highly dependent on the data provided by theuser. Users have the ability to make changes to the database structure of the energy system model.These structural changes can include modifying resource supplies, energy conversiontechnologies, end-use demands, and end-use technologies used to meet these demands.MARKAL represents all energy producing, transforming, and consuming processes as aninterconnected network called a Reference Energy System (RES), as shown in Fig. 2.1.

Figure 2.1: Reference Energy System for MARKAL


10/65

One of MARKALs greatest strengths is its flexibility and potential for rich technological detailfor both supply-side and demand-side technologies. However, this degree of detail in representinga range of technologies must come through user input of parameters used to model these devices.To reduce the time and effort needed for these inputs, databases can be maintained and importedinto the model that contain these parameters for a wide range of technologies. Once these valuesare entered into the model, MARKAL determines the least-cost method of meeting demand

subject to the users constraints (Shay, 2006). Due to MARKALs LP structure, the model isprogrammed using the General Algebraic Modeling System (GAMS) system and is designed tofind the minimum of the discounted total energy systems cost over the planning horizon. Theobjective function of total system costs include:

Annualized investments in technologies (e.g., capital costs) Fixed and variable operation and maintenance costs Costs of exogenous energy and material imports and domestic resource production (e.g.,

mining of natural resources)

Revenue from exogenous energy and material exports Fuel and material delivery costs Any additional taxes assigned to environmental emissions

Outputs from MARKAL include information regarding the supply- and demand-side technologycapacity and activity, system costs, shadow prices for binding constraints, energy services andprices, and emissions data. These results allow the user to evaluate the impact of a giveninvestment or policy on the evolution of the energy system over time (Shay, 2006). This researchapplies this model to estimate the impact of CCS technologies on the US energy mix between2000 and 2050, finding which policy and technology development scenarios are most conduciveto CCS deployment and determining how the presence of CCS options decreases greenhouse gasmitigation costs.

Due to the least-cost linear optimization structure of MARKAL, small differences in parameters(especially those with high degrees of uncertainty) can have a disproportionately oversized andunrealistic impact on technology development projections. Another important characteristic ofoptimization-based models like MARKAL is that, even though two technologies may be verysimilar in cost, bang-bang solutions may result, in which all of one technology is deployed butnone of another. These technology change characteristics of the model should be kept in mindwhen interpreting the results of models such as MARKAL.

2.2. EPA MARKAL Database

Due to the data-driven nature of the model, MARKAL requires extensive user inputs to model anenergy system in detail. These inputs typically come in the form of databases, which have enoughdetail to represent the energy system under investigation. In order to investigate CCS in a UScontext, this research relies on the United States Environmental Protection Agencys MARKAL

Technology Database and Model (EPANMD) (Shay, 2008). This extensive database is used andmaintained by the EPAs Energy and Climate Assessment (ECA) team. Based in the NationalRisk Management Research Laboratorys Air Pollution Prevention and Control Division, thisgroup uses the database to analyze drivers of technology change and their implications onemissions and air quality.

The technology-rich database contains detailed representations of the major components of theUS energy system, including the commercial, industrial, residential, transportation, and electricitygeneration sectors. This project uses the newest version of the database, which is calibrated to


11/65

2008 data from the Energy Information Administrations (EIA) Annual Energy Outlook and theNational Energy Modeling System (NEMS). Technology data not included in these sources comefrom widely-recognized authoritative sources such as the Electric Power Research Institutes(EPRI) Technical Assessment Guide and the DOEs Office of Transportations Quality Metricsreport. The database contains detailed information on the US energy system including:

Useful energy end-use service demands (which are exogenously specified) Available resource supplies and costs Technology characteristics (including existing capacity, cost details, and performance

data) for both supply- and demand-side technologies

Constraints and taxes on technologies and emissions (based on current legislation, likethe Clean Air Act)

2.3. Model Scenario Development

MARKAL represents the US energy-economic system, but in order to investigate climate changepolicy outcomes, we had to develop and incorporate emissions constraint scenarios into themodel. These scenarios are based on the policy framework derived by the United States ClimateChange Science Program in their 2007 synthesis paper Scenarios of Greenhouse Gas Emissions

and Atmospheric Concentrations. The authors of this report have two goals: 1. Explore theimplications of global scenarios with alternative emissions stabilization levels, and 2. Considerthe economic and technological aspects of the response. They employ three IntegratedAssessment Models: IGSM, MERGE, and MiniCAM.

Table 1: Radiative forcing level scenarios (Clarke, Edmonds, Jacoby, Pitcher, Reilly, & Richels, 2007)

Total RadiativeForcing fromGHGs in thisResearch (W/m2)

ApproximateContribution toRadiative Forcingfrom non-CO2GHGs (W/m2)

ApproximateContribution toRadiative Forcingfrom CO2(W/m

2)

CorrespondingCO2Concentration(ppmv)

Level 1 (L1) 3.4 0.8 2.6 450Level 2 (L2) 4.7 1.0 3.7 550Level 3 (L3) 5.8 1.3 4.5 650Level 4 (L4) 6.7 1.4 5.3 750Year 1998 -2.1 0.65 1.46 365Pre-Industrial(1750)

- - - 278

The authors build a reference scenario (with no commitments beyond Kyoto), as well as fourmitigation scenarios that aim for stabilization at various levels of anthropogenic effect on thebalance. The foundations of these scenarios are calculations of the quantity of long-term radiativeforcing that correspond to various atmospheric CO2 stabilization concentrations (Clarke,

Edmonds, Jacoby, Pitcher, Reilly, & Richels, 2007). L1 corresponds to a stabilization level of 450ppmv of atmospheric CO2, L2 550 ppmv, L3 650 ppmv, and L4 750 ppmv. These values andtheir corresponding levels of radiative forcing are shown in Table 1 above. The authors then use asuite of integrated assessment models available earlier to plot out emissions trajectories undereach of these different emissions reference levels. We employ the reference case and the fourstabilization levels as modeled by MiniCAM in our analysis. For the business-as-usual (BAU)scenario, we use the scenario represented in the EPA Database. This scenario represents a medianbetween other BAU emissions scenarios published by WRI, MERGE, MiniCAM, and IGSM.


12/65


13/65

3. CCS Model Representation

3.1. CCS Technologies in MARKAL

The EPANMD representation of CCS technologies focuses primarily on CO2 capture, while usinga simple flat cost estimate for transport and sequestration (Shay, 2008). This model assumption isbased on the belief that the capture portion of CCS will comprise the most significant portion ofCCS-related costs. Additionally, CO2 storage is assumed to be driven by policy and geologicalissues that fall outside of the domain of the MARKAL energy systems framework.

The database already models the three primary CCS pathways mentioned in Section 1.2 (i.e.,post-combustion capture, pre-combustion capture, and oxy-fuel combustion) within the electricitysector module. The three CCS options for new capacity builds for baseload electricity productionare: IGCC plants with CCS, oxy-fuel plants with CCS, and Natural Gas Combined Cycle(NGCC) plants with CCS. In addition to new capacity with CCS, the database also includes CCSretrofits for existing plants: IGCC plants, NGCC plants, and a range of existing subcritical coalplants (which are based on the sulfur content and coal type used at the plant). The model currentlyonly allows new post-combustion amine capture to be used as a retrofit option, since newsupercritical plants with CCS are assumed not to be competitive with IGCC or oxy-fuel.

For GHG emissions reductions scenarios, the CCS plants in MARKAL compete against otherlow-carbon supply-side technologies as potential mitigation sources. As detailed in the Chapter 4of this report, CCS competes mostly with wind and advanced nuclear electricity generation.

The representations of oxy-fuel and IGCC capture plants have nearly identical parameterizationsin the model. Since both plant types are still in their incubatory technological development stages,most literature sources have overlapping cost and performance values for these technologies.Both technologies are included in the model for completeness, though the fact that optimizationmodels like MARKAL may exaggerate small differences in uncertain parameters to yieldoutsized deployment means that model interpretation regarding the split between thesetechnologies must be carefully tempered.

The EPANMD contains retrofit options for new steam coal plants (including subcritical andsupercritical designs), residual coal plants (that existed at the beginning of the time horizon), andnew IGCC and NGCC plants. Instead of being modeled as conversion technologies (i.e., newelectricity generation plants), retrofits sit in the model as process technologies that are upstreamof their corresponding generating technologies. Since capture capabilities are dependent onhaving a low sulfur flue gas stream, retrofit technologies are in-line with flue gas desulfurization(FGD) retrofits, which implies that both options must be installed for CCS to be pursued. SinceMARKAL chooses technological options with the lowest cost, this model characteristic canproduce exaggerated estimates for the availability and deployment rate of specific technologies,

which may produce rapid and unstable shifts in period-to-period technology selection. Therefore,the database representation of retrofits is fairly uniform across plant types, since modelingidentifiable but marginally different retrofit schemes can have meaningless and potentiallymisleading results (Shay, 2008).

The EPANMD uses a single flat fee to represent the cost of CO2 transport, injection, and long-term monitoring. This figure is incorporated into the model as an associated environmental coston sequestered carbon at a price of $28 per metric ton of CO2. This highly conservative value


14/65

falls at the upper end of published estimates and assumes geological injection sites that areavailable within 300km of large-scale power plants. The database limits CCS market penetrationthrough upper bounds on captured CO2 (COS), essentially capping the aggregate amount of CO2sequestered per model time period. Since CCS will require regional approval of uncertainties likeinfrastructure, regulatory, and policy issues before wide deployment, the database boundsrepresent best-guess values for the limited penetration of CCS capacity increases for given

periods of time. These constraints are often binding in strict GHG policy scenarios.

3.2. Model Incorporation: Retrofits

3.2.1. Background

Given that the time and cost required to replace the entire CO2 emitting portion of the US powergeneration sector would be inordinate, CO2 capture retrofits for current power plants may berequired to meet stringent emissions caps. The most likely form of CCS retrofits would come inthe form of an additional flue gas treatment similar to SOx and NOx scrubbers installed in the USsince the 1970s (Singh, 2003). Currently, the most established technology to accomplish such atreatment would be amine capture systems. This form of CO2 capture has been deployed on asmall scale for decades in sour gas treatment but has never been demonstrated on a scale requiredof a typical 500 MW plant (DOE/NETL, 2007).

These systems use counterflow columns of amine solution raining down on the exiting flue gas(Singh, 2003). The amine solution chemically bonds with CO2 to form a carbamate and is thensent to another column where it is heated to drive the CO2 out of solution. A pure CO2 stream issent out of the column ready for compression and storage, while the regenerated amine solution issent back to the original column to capture more CO2. This process requires roughly half of asteam power cycles steam to be diverted to provide the heating for amine regeneration, whichsignificantly lowers the electricity capacity of the power plant. This energy penalty requiresplants retrofitted for CCS to be derated, lowering both the efficiency and capacity of the facility.Amine systems also require a very good flue gas desulphurization (FGD) system to remove SOxfrom the products stream before entering the amine unit, since it would also bond to the amines

and not be able to be driven out. These plant additions make CCS retrofits a very costly measureto be undertaken (DOE/NETL, 2007).

Due to the high cost to retrofit plants, utilities may opt to build a new plant over retrofitting acurrent one. The plant economics will play a large factor in this decision, depending on the age ofthe plant, amount of emissions, cost of carbon, and many other factors. However, even if retrofitsare an economically competitive abatement alternative, utilities may not have the option toretrofit existing plants. The amine capture system would need a certain amount of space directlynext to the power plant to avoid transporting hot flue gases over considerable distances (GHG,2007). However, this space may not be available in residential neighborhoods or heavilyindustrialized areas. There also must be reasonable access within the power generation facilityitself, which may be packed tightly together and can make it hard to reroute flows as necessary.

Large capture equipment must also be delivered, which could be difficult at some plants due totheir surroundings. Some parts could be delivered separately and constructed on-site, but thisalternative would lead to higher retrofit costs. There are also a number of system demands leadingto decreased turbine performance, increased cooling water, and water treating requirements, notto mention reasonable routes to suitable storage sites.

Given the large number of constraints on power plants that may limit reasonable retrofit costs, itis necessary to limit the amount of retrofits that may be performed in a modeling scenario. While


15/65

no detailed study currently exists that details how many power plants in the US would be eligiblefor CCS retrofits, one study on candidate plants roughly estimates that184 GWe of existingcapacity could be retrofitted (EPRI, 2009). This number is based on the age and size of plants inthe existing fleet of plants. However, not all of these plants would meet the previously mentionedconstraints, so a final estimate of plants that are actually available for CCS retrofits is 60-90 GWeof coal power plants. This estimate only considers coal-fired power plants, since numerous

studies find the cost of CO2 avoided for coal plants considerably less than that of natural gasplants (GHG, 2005), which is largely due to the higher concentration of CO 2 in the flue gasstream from coal plants.

A large amount of planning has gone into designing new power plants that will be capture-ready due to the large number of constraints, additional costs, and down time of retrofittingpower plants (GHG, 2007). This means that they would have turbines sized to perform optimallyafter retrofit, supporting systems, such as water, oversized initially to handle increased loads afterretrofit, and the capacity to meet space constraints for capture systems installed later on. This alsorequires a detailed look into nearby storage sites and possible routes from plants to those sites.The additional cost of oversizing many systems within the power plant adds additional capitalcost to the initial plant install; however, this cost combined with the cost of retrofit is less than the

same costs of a BAU plant and retrofit. The additional capital cost is also relatively smallcompared with the total cost of the plant. This pre-investment in a capture ready system not onlylowers the total capital cost after retrofit, but also leads to a shorter down time of the plant for theretrofit to take place and (more importantly) a lower derated capacity. Even though efficiency ofthe BAU and capture ready plants before and after retrofit are virtually the same, the capacity ofthe BAU plant takes a 31 percent reduction in capacity while the capture ready plant suffers onlya one percent reduction (DOE/NETL, 2007).

CCS retrofits could play a large role in utilizing the existing power generation fleet in a carbon-constrained market. Pre-investment in capture ready facilities may also play an even bigger rolein the near future as the uncertainty of just how carbon constrained the market will become andwhen those constraints will be applied remains.

3.2.2. Literature Review

An investigation of retrofits to existing coal power plants was undertaken by National EnergyTechnology Laboratory (NETL) to determine the extent of market penetration under variouscarbon prices (DOE/NETL, 2008). This analysis uses the National Energy Modeling System(NEMS) to model the costs of retrofits. The analysis found limited penetration of CCS retrofitsuntil a carbon price over $30/t-CO2e was established. Once the carbon price was $60/t-CO2e, halfof the existing coal fleet was retrofitted by 2030. This is an upper-bound on deployment, as therewere no site-specific calculations made. Retrofit deployment was also analyzed with increasednatural gas prices, which saw an increase in the number of retrofits installed, but still did notallow for any sizable deployment for a carbon price under $30/t-CO2e.

A follow-up study also completed by NETL using NEMS investigated the option of repoweringcoal plants with integrated gasification combined cycle (IGCC) with CCS (DOE/NETL, 2008).The analysis found similar amounts of deployment of IGCC with CCS as were seen with coalretrofits at a carbon price of $45/t-CO2e. They also found a roughly equal deployment of nuclearpower plants. Again, no deployments occurred until the carbon price exceeded $30/t-CO2e.

A thorough investigation of capture ready plants was completed by NETL (DOE/NETL, 2007).Four types of plants supercritical pulverized coal (SCPC) BAU and capture ready as well as


16/65

IGCC BAU and capture ready, were modeled and then retrofitted. A detailed summary of costsand performance parameters were considered in addition to plant financing. It was determinedthat SCPC capture ready plants were economically advantageous to BAU plants if retrofittedwithin the first 10 years after initial installment. This was also true of IGCC if retrofitted withinthe first seven years of operation. The shorter window of opportunity for IGCC came as a resultof smaller retrofit costs for both BAU and capture ready plants.

A similar study to our investigation was completed regarding CCS penetration in the Netherlandsunder different policy scenarios (Vrigmoed, 2009). A bottom-up model without perfect foresightshowed that moderate CCS penetration occurred under the various policies analyzed including:CO2 price, CCS subsidies for capital costs, feed-in tariffs for CCS and renewable energy, and aCCS obligation. It found that the most cost-effective policies were to stimulate both CCS andrenewable energy simultaneously. Reducing initial transport costs for CO2 to a level that wouldbe more likely once a distribution network were established increased deployment, especially inCCS coal plants. Capital cost subsidies and feed-in tariffs were also most beneficial to theincreased market penetration of coal plants with CCS.

3.2.3. Policy Implications

CCS retrofits may play a large role in future climate policies. Different scenario assumptions willlead to diverse roles for CCS, from indirect effects of a carbon tax causing many utilities to buildand retrofit with CCS, to direct effects if a cap on CO2 emissions from plants is introduced. Withmost considered policies involving a price on CO2 emissions either through a tax or through capand trade, the cost of plant operations could go up immensely to the point that the cost of CCSbecomes less than the cost of paying for emissions to stay as is. This is the case off the coast ofNorway, where a $55/t-CO2 carbon tax in 1991 caused the Sleipner natural gas extraction tobegin sequestering CO2 emissions resulting from the treatment of the acid gas extracted from thefield below (Hawkins, 2009). With roughly one million tons of CO2 captured each year, thisstrategy had a two year payback period.

Decisions like Sleipners may lead to large deployment of CCS in the US electricity sector,depending on how the carbon price compares to cost of CO2 avoided. With initial costs for CCSrelatively high relative to proposed carbon prices, government investment in the first wave ofCCS installments is needed to prove the technology at a large scale and lower subsequent plantinstallments through learning effects. These cost reductions through learning could also be aidedby a sharing of knowledge regarding CCS installment and operations. This technologicalknowledge transfer would also help on a global scale to share information freely with othercountries, especially those nations that are heavily dependent on coal.

Uncertainty in future carbon prices could lessen the effect that CCS may play in the US, given thehigh capital costs and plant derating associated with CCS. If utilities are unsure about potentialreturns on investment with CCS, they may be more willing to use alternatives that require lessexpensive initial outlays for capital installation. This scenario is ideal for a larger deployment ofcapture-ready plants with a slightly higher initial cost, but with much less of a capacity reductiononce CCS is installed, a shorter downtime for the retrofit, and a lower cost to retrofit. Capture-ready installments could also be rolled out on a large scale if a future carbon price is expected inthe near-term after a plant will be installed. Utilities could use the plant to hedge against a futurehit on revenue, especially since plants are expected to operate for at least 20 years with a longpayback period. Even using a direct carbon pricing policy (e.g., a carbon tax) exposes plantoperators to the political fluctuations that occur as the controlling powers in the government shiftevery few years.


17/65

Given the general publics opposition to new coal plants and challenges in obtaining permits fornew coal installations, another policy option is a mandate that new plants must use CCS. Thiswould most likely lead to an increase in retrofits in the near-term as the capital costs would beless than that of building a new plant with CCS. Once capacity and public acceptance for CCSincreased, this could lead to larger CCS deployment. It may also lead to an extension of the lives

of many existing coal-fired plants if no carbon price is in place.

Another policy that could have large implications for CCS retrofits is a cap on emissions from allplants. This would effectively require new plant builds with CCS and CCS retrofits or theretirement of current plants. Since the lead time for all new plant installments with CCS would belong, retrofits of current plants would be required on a large scale. This policy might have thelargest impact on CCS retrofits until new low emission technologies could be developed. All ofthese policy environments were explored in the scenarios used for this analysis, as discussed inthe next chapter.

3.2.4. Model Incorporation

A few changes to the model are required to better represent CCS retrofits in the MARKAL modelusing the EPA database. In order to limit the possible overestimation of retrofits that may occur toexisting power plants, a conservative 60 GW upper bound was put in place to limit the amount oftotal CCS retrofits to BAU plants (EPRI, 2009).

In order to allow the model to take advantage of capture-ready facilities to hedge against futurecarbon limits, cost and performance data were acquired for SCPC and IGCC plants with thecapture-ready option before and after conversion has taken place (DOE/NETL, CO2 CaptureReady Coal Power Plants, 2007). Cost data were also obtained from the same source for dataregarding BAU plants with and without retrofit. These values were used as a metric to match thecapture-ready data to other power plants already in the EPA database. Since the capital costs ofretrofits in the model are slightly higher than most published sources would estimate to offset

cheaper BAU power plants, the new capture-ready data was aligned with these values so that onetype does not dominate the other type of plant in the cost minimization model. Retrofits areoverpriced in the model to ensure that it is more expensive than a plant built with CCS installedfrom the beginning. The BAU plants have lower costs than published studies but are meant to beaccurate relative to each other.

The plants are incorporated into the database in a similar manner to FGD systems, which areinstalled in the fuel chain prior to a CCS retrofit on a BAU plant. This will ensure that capture-ready plants are installed prior to being retrofitted and that a retrofit cannot be installed withoutpreviously installing the main plant. This is similar as to how BAU retrofits are dealt with in theEPA database.

Given the perfect foresight structure of the model, no capture-ready plants will be installedwithout being subsequently retrofitted for CCS, since the capital cost of capture-ready plants ishigher than that of a BAU plant. Therefore, scenario runs will not be able to capture the fulleffects of utilities hedging against a future carbon constraint. Instead, these effects appear when afuture carbon price escalation leads to faster CCS deployment than can be achieved by all newplant builds or by hitting the maximum number of retrofits of BAU plants.


18/65

3.3. Model Incorporation: Partial Capture

3.3.1. Background

CCS is a technology that will be an important potential strategy to reduce GHG emissions,especially from point sources. However, most studies assume that CCS-equipped plants willcapture 90 percent of emitted CO2, which is commonly referred to as full capture. On the otherhand, a less frequently discussed alternative is known as partial capture, which can refer to anyCCS system will lower than a 90 percent capture efficiency. Full and partial capture systems havemarkedly different technological characteristics for parameters like plant efficiency, variableoperation and maintenance (O&M) costs, fixed O&M, and consequently on cost of electricity.For example, the Intergovernmental Panel on Climate Change (IPCC) estimates that installationof full capture will increase the capital cost of a new SCPC power plant by 70 percent anddecrease the new SCPC plants efficiency by 25 percent(Intergovernmental Panel on ClimateChange, 2005). However, studies and models on CCS typically only focus on full capture.

Since partial capture will have lower capital and O&M costs than full capture, it may provide amore cost-effective way of abating CO2 emissions from coal-fired power plants. However, sincemany of the performance and cost benefits of partial capture have not yet been extensivelystudied, the goal of this section of our analysis has four primary objective:

Calculate how partial capture percentages impact the cost of electricity (COE) Determine the value of partial capture Explore how partial capture impacts the future US energy system under different climate

scenarios

Perform sensitivity analysis on our resultsThe study will focus on CCS with three types of new power plants: SCPC, IGCC, and NGCC.

3.3.2. Literature Review

The section explores two major concepts detailed in current partial capture literature: 1. Estimatesof the cost of electricity from full capture plants; and 2. Expected performance characteristics of

partial capture.

CCS is currently deployed on a very limited scale. Therefore, actual cost of electricity data ofCCS is not readily available. Instead, many papers have tried to estimate the expected cost ofelectricity while incorporating CCS. IPCC has compared the estimated COE from various sources (Intergovernmental Panel on Climate Change, 2005). Table 3.1 summarizes the COE of SCPC,IGCC and NGCC with full capture. In this section, only new plants have been studied. All givencosts are for capture only and do not include the cost of CO2 transport and storage which ismodeled separately in MARKAL.


19/65

Table 3.1: Summary performance of new plant with full capturea

SCPC Plant IGCC Plant NGCC Plant

Min Max Rep Min Max Rep Min Max RepPlant Efficiency, LHV basis (%) 30 35 33 31 40 35 47 50 48

TCR without capture ($/kW) 1,161 1,486 1,286 1,169 1,565 1,326 515 724 568

TCR with full capture ($/kW) 1,894 2,578 2,096 1,414 2,270 1,825 909 1,261 998

COE without capture ($/MWh) 43 52 46 41 61 47 31 50 37

COE with full capture ($/MWh) 62 86 73 54 79 62 43 72 54a TCR = total capital requirement; Rep = representative value; all costs represent real 2002 US dollars

The elements of the levelized COE can be divided into three groups. The first component is theenergy penalty. As mentioned in previous sections, CCS technologies impose parasitic energylosses to operate, which lower plant efficiency and electricity output. Decreased electrical

capacity can be modeled as increased variable O&M and fixed O&M. Decreases in plantefficiency are accounted for through an energy penalty, defined by the following equation, where is the net plant efficiency.

= 1

Another result of incorporating CCS is increasing capital cost. In Table 3.1, CCS is shown toincrease total capital costs by 50 percent, which is the largest contributor to the cost of electricity.The COE may be reduced for CCS-equipped units under carbon price policies due to loweremissions costs compared with non-CCS units.

Hildebrand has investigated the characteristics of partial capture on SCPC and IGCC powerplants (Hildebrand 2008). This article assumes a constant coal and natural gas feed. The capitalcost for newly sized equipment (B) compared to the baseline value (A) is given by the equation:

=

The coefficients are estimated using NETL data. The relationship between capture percentage andturbine output is assumed to be linear. As the capture percentage increases, more steam must beextracted, which results in linearly decreasing output. The demands for capture solvent (i.e., MEAfor SCPC and NGCC plants; selexol for IGCC plants), limestone, and water also varies linearlyaccording to capture percentage. The summary of the cost estimation of partial capture both in

SCPC and IGCC plants from Hilderbrand can be found in Table 3.2. The result is normalized tothe COE of a BAU SCPC plant, since we are interested in the relative costs between partialcapture and full capture. With full capture, IGCC has a cost advantage over SCPC, whereas anSCPC plant is more cost efficient than IGCC plant in the reference case.


20/65

Table 2.2: Comparison of Costs at 0 percent, 45 percent, and 90 percent capture for an SCPC plant andIGCC plant. All costs are normalized to non-CCS SCPC plant

Percent Capture SCPC Plant IGCC Plant

Normalized Unit (per net kW) Cost Normalized Unit (per kW) Cost

0 percent 1 1.15

45 percent 1.43 1.32

90 percent 2.06 1.52aAll costs are normalized to non-CCS SCPC plant values


Partial capture could have a large impact on energy systems in US under carbon constraints. Atotal of 59 proposals of coal plants were cancelled, postponed, or put on hold (SourceWatch, 2007)in 2007. The concerns about climate change and potentially high plant costs are the main reasonsfor these cancellations, which can partly explain why natural gas plants have been preferred tocoal plants. If coal plants with 50 percent partial capture are available, they have equivalent CO2

emissions per electricity output as NGCC plants.

Moreover, coal plants can strengthen US national energy security. The US can support its entirecoal demand domestically, whereas around 15 percent of natural gas consumption consisted ofimports in 2007(EIA, 2008). Coal plants with partial capture can strengthen national energysecurity by decreasing natural gas imports while having the same emissions as natural gas. Insummary, partial capture plays the largest role in influencing the choice between natural gas andcoal, especially under scenarios with deep uncertainty.

3.2.4. Model Incorporation

45 percent partial capture of SCPC, IGCC, and NGCC were added into the MARKAL database.

In addition to Hildebrands work, partial capture with NGCC is included in the study. Importantmodel parameters have been calculated using our newly designed Partial Capture Model. ThePartial Capture Model is an Excel-based spreadsheet model to estimate the key characteristics ofpartial capture in three types of plants from 0 percent to 90 percent capture. Key characteristicsinclude the levelized cost of electricity, carbon emission rates, and net plant efficiency. Thedetails of the Partial Capture Model can be found in Appendix A. Table 3.3 contains a summaryof technical and cost properties of 45 percent partial capture in three types of plants. Each value isadjusted via relative cost conversions to match the original MARKAL CCS database.


21/65

Table 3.3: 45 percent partial capture key parameters for MARKALa

SCPC Plant IGCC Plant NGCC Plant

Investment Cost ($/GW) 1,540 1,805 932

Fixed O&M Cost ($GW-1 yr-1) 32.2 34.7 14.4

Variable O&M Cost ($/PJ) 1.45 0.95 0.34

Emission Coefficient (thousand tons of CO2/PJ) 81.6 90.4 50.3

Energy carrier input (PJ/PJ) 2.92 2.26 2.02aAll costs represent real 2000 US dollars

Only 45 percent partial capture of SCPC, IGCC and NGCC are added into the MARKAL due totime and model constraints. 45 percent capture was chosen for model incorporation, since it is thecapture level where the coal plants CO2 emissions are equivalent to that of a natural gas plant.

3.4. Model Incorporation: Storage

3.4.1. Background

As described in Section 1.2, there are several candidates for the storage of CO2 captured frompoint sources. These reservoirs include oil fields, gas fields, saline formations, and unminablecoal seams. CO2 is already injected into old or declining oil and gas fields to increase productivityin a process known as enhanced oil recovery (EOR). As the IPCC notes in their Special Report onCarbon Dioxide and Storage, nearly 50 million metric tons of CO2 (MMTCO2) are injectedannually into these fields on a global scale, so the process is neither novel nor rare(Intergovernmental Panel on Climate Change, 2005). EOR is by far the most cost-attractive of theconsidered options, because it actually saves on production costs and the geological structure oftraditional fields is so familiar. Unminable coal seams can be used to store CO2 through a processknown as enhanced coal bed methane recovery (ECBM), in which the gas is injected directly intobeds of bituminous coal and trapped methane is collected. However, a recent study reveals thatECBM is not commercially viable at current costs (Schroeder, Ozdemir, & Morsi, 2002). Deepsaline formations are by far the most abundant and the largest storage sites by volume. Yet theyare poorly understood from a geological perspective, especially as their structure relates topotential leakage. Just as there are several candidates for reservoir type, there are a few differenttechniques for geo-sequestration, each of which poses its own particular challenges. A fewinclude super-critical sequestration, depositing CO2 in fluid form, and direct injection.

Besides experience with industrial process, storage of CO2 is a natural phenomenon, and there isa great deal of geological capacity beyond that currently being used. Depleted oil and gasgeological formations represent roughly hundreds of GtCO2 of potential storage supply, and deep

saline formations in the thousands or tens of thousands of GtCO2. In addition, injection strategiesare currently employed in the oil and natural gas industries, so that technical hurdles are minorand the costs are relatively small, at $0.6-8.3/t-CO2 stored (Intergovernmental Panel on ClimateChange, 2005). This has led the IPCC to conclude in their special report that nearly all CO2currently emitted by point sources could be feasibly stored for millions of years. Of that amountstored, 99 percent retention over the next 1,000 years is feasible and the standard of futurestorage. Given the promise of CCS storage, we next narrow the focus to the political andeconomic dimensions of storage within the US.


22/65


23/65

As illustrated in Figure 3.1, the costs of these approaches vary wildly and strongly drive the typeof candidate storage that is most attractive at various levels of mitigation. At the lowest levels,EOR gains revenue from additional hydrocarbon recovery, making it a cost-saving option (i.e., anegative net storage cost). At higher levels, the incredible abundance of deep saline formationseffectively caps the upper limit of storage and transportation costs at $12-15/t-CO2.


CCS technology is relatively politically feasible, as it allows CO2 emissions from large sourceslike power plants to be mitigated without jeopardizing existing industry or incurring exorbitantcosts. Consider the Department of Energys Carbon Sequestration Regional Partnership program,a dedicated government program to reveal future storage capacity (Friedman, 2006). Putting asideexisting political goodwill, the question of where this captured CO2 can reside and howeffectively and safely it will be stored is politically thorny.

3.4.3.1. Feasibility and SafetyAssumed feasibility of large-scale storage is based on existing industrial experience with EOR,but we have limited experience with geological storage in other underground environments

(Intergovernmental Panel on Climate Change, 2005). This means that the potential for leakage ishighly uncertain. New geological sites storing huge quantities of CO2 could potentially result incontinuous leakage of CO2 from these sites over time, throwing the whole mitigation value of theexercise into question.

Stored carbon also poses a small risk to human and environmental welfare. The IPCC reportadmits that an abrupt leakage scenario is possible through an abandoned well or through a wellfailure. This could lead to lethal effects on plants, contamination of groundwater, and high CO 2concentrations in the atmosphere threatening animals and people (Intergovernmental Panel onClimate Change, 2005). If this sounds far-fetched, consider the case of Lake Nyos in Cameroon.In 1986, a huge quantity of naturally sequestered CO2 was released, resulting in the death of1,700 people. Although this scenario is relatively improbable, its mere existence demonstrates a

political weakness. Many politicians will likely hesitate to propose storage near the homes of herconstituents when such a risk, real or imagined, is voiced. Concrete risks to the habitats, water,and livelihoods of communities near geological sites will impede their installation, and legal andregulatory issues remain unresolved.

3.4.3.2. The Geography of Storage CapacityStorage facilities are not evenly distributed across the US. The National Energy TechnologyLaboratorys 2008 Carbon Sequestration Atlas confirms that significant oil and gas fields arerestricted to a few states such as California, Ohio, New Mexico, Texas, North Dakota, andWyoming. Conversely, the entire northeastern region contains little to no capacity. Collectively,these fields are abundant, and represent between 82.4 and 138 GtCO2 (United States Departmentof Energy, 2008). Deep saline formations represent an order of magnitude more of potentialstorage (some 3300 GtCO2) and are located across the entire country. Still, there are some areaswithout access to either immediate source such as Virginia, Connecticut, and Missouri. Betweenoil and gas reservoirs, saline aquifers, and coal seams, NETL states that, at least one of each ofthe three main types of types of potential geological reservoirs for CO2 occurs across most of theUnited States in relative proximity to many large point sources of CO2 (United StatesDepartment of Energy, 2008).

There are many policy complications. The cheapest options may not always be located nearby,and saline formations are still at a level of high cost and low technical proof. This implies that


24/65

geography may make transportation and storage pricier than the least-cost option models propose,especially in those areas located far away from the cheapest EOR sites our model highlights.Moreover, large-scale distribution of CO2 requires an extensive pipeline network and this raisesimportant economic, policy, and safety dimensions of its own. A CO2 travel network alreadyexists for transport of gas to EOR sites, implying that no part of the country is out of range ofstorage. However, a sudden increase in the quantities and destinations of said gases poses a

roadblock to future implementation because of complex regulation (Parformak & Folger, 2008).

Since our MARKAL configuration assumes the US to be a singular energy-economic system,these regional policy implications do not appear in our results. The Battelle/JGCRI cost curve weuse already accounts for the issue to some extent by pairing existing sources and sinks; thisaccounts for the general geographic distribution. However, it does not fully account for thecomplex decisions that will face point source operators trying to gauge a least-cost storage optionin the coming years.

Figure 4: North American po int sources (left) and storage reservoirs (right) (Dooley, Dahowski,Davidson, Bachu, Gupta, & Gale, 2004)

3.4.4. Model Incorporations

The costs related to storage of captured CO2 should be represented through storage supply curvesanalogous to those detailed in the section 3.4.2. However, the EPA database employs a flat cost of$28/t-CO2. Storage supply or cost curves incorporate how the cost of storage changes as thequantity of supplied CO2 from point sources varies. Supply curves thus offer a more accurateview of how large-scale CCS can compete with other forms of emissions abatement if widedeployment leads to greater stresses on available storage sites. The abundance of storage optionsrelative to demand even in a high CCS use scenario- 3,500 GtCO2 as compared to 62.5 GtCO2 -means that only the least cost options are relevant (Dooley, Dahowski, Davidson, Bachu, Gupta,& Gale, 2004). In turn, industry will focus on the following options:

Depleted Oil Fields: They are relatively abundant and the cheapest option, as theyrepresent a value-added (cost negative) storage option due to revenues gained from

hydrocarbon recovery. At relatively low levels of CO2 deployment, they represent themost cost-attractive option and are filled up first.

Deep Saline Formations: Although costly, they are everywhere. Once cost negativeoptions are exhausted, they will likely compose the vast majority of future storage sitesdue to their prevalence and thus low transport costs.

In order to represent these options during each year, we construct a yearly cost function that plotscost based on capacity. Define X as Cumulative Annual Mt CO2 stored in US Reservoirs in the


25/65

range of 0-3000 Mt. The following stepwise function defines costs/ton of CO2 transported to andstored within all candidate reservoirs:

Xa(x) = $-7.00 if 0


26/65

4. Results

4.1. Original Database Runs

4.1.1. Business-as-Usual (BAU)The base model using the EPA database was investigated with no implemented policies in orderto achieve a BAU case with which to compare future scenarios. As seen in Figure 4.1, there wassteady growth in CO2 emissions from all sectors except for residential, which stays roughlyconstant. Emissions grow steadily through 2035 and then escalates at a higher rate until the endof the time horizon. This growth in emissions is not as large as it would be if only technologiespresent the year 2000 were available over the entire time horizon. Efficiency improvements resultin the bulk of this reduction, as represented by the dashed-dot line. It is also apparent that CCSand biomass reductions are not incorporated in a BAU case as both lines remain at zero on the x-axis. The two largest emitting sectors are by far those of electricity production and transportation.These are therefore the largest targets for wide-scale reductions in the future. As statedpreviously, the electricity production sector will be the focus of this investigation.

Figure 4.1: System-wide and sectoral CO2 emissions for the BAU scenario

Given the current focus on CCS market penetration, one of the most important model outputs isthe generation technology portfolio breakdown for the electricity sector. Figure 4.2 showselectricity generation in trillion kWh broken down by technology over time. Overall, this revealsthat the projected growth in electricity demand is large, nearly doubling in 50 years. This growthin electricity is projected based on the technologies employed to meet end-use demands. Whilethe end-use demands are inelastic in each scenario, the choice of technologies and thus energycarriers is determined by the cost minimization function, so the electricity demand may changebetween scenarios. The legend also displays the candidate technologies available to meet that

-4,000

-2,000

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2000 2010 2020 2030 2040 2050

Carbon

Diox

ide

Em

iss

ions

(MMTCO

2)

Electricity Production

Industry

Commercial

Residential

Transportation

After CO2 intensity

reductionAfter efficiencyimprovements

Constant techs andfuels

Capture & Storage

Biomass


27/65

electricity demand. While there are many options available, only a handful are utilized in theBAU case. It is clear that existing coal has a large portfolio share, even though many of theseplants may be operating well beyond their planned lifetimes. Plant age and projected retirementsare not modeled in the current EPA database directly, and there is therefore no limit to how longexisting coal plants may operate within the 50-year time horizon of the model. This assumption isnot very realistic, as many existing coal plants are already 50 years old. Some deployment of new

SCPC plants occurs, which offer increased efficiency while still using coal as a cheap feedstock.It is clear that in the BAU scenario, coal is the dominant fuel in electricity production. It is alsoimportant to reiterate that no CCS technologies come into play when a carbon price is not present.

NGCC plants also have a large part to play in the generation landscape. However, they do notexpand beyond the constraints set to match actual 2005 deployment levels. Nuclear plays asimilar role, barely expanding beyond current installment levels over the time horizon. Given thatthe best hydropower resources have been tapped in the US, it is not surprising that hydro does notexpand either. Without a carbon price, renewable resources have almost no part to play ingeneration, barely expanding past current levels in 2025 and 2030. Interestingly, municipal wasteproduces an order of magnitude more electricity than renewables in the base case.

Figure 4.2: Electricity production by technology for the BAU scenario

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2000 2010 2020 2030 2040 2050

Elec

tricity

Genera

tion

(Tri

llion

kWh)

Distributed Solar PV

Central Solar PV

Central Solar Thermal

Wind Power

Hydropower

Geothermal Power

Municipal Waste to Steam

Biomass to IGCC

Biomass to Steam

Advanced Nuclear Power

Conventional Nuclear Power

Residual Fuel Oil to Steam

Diesel to Combined Cycle

Diesel to Combustion Turbine

NGA to Combined-Cycle-CCS

NGA to Combined-Cycle-CCS Retro

NGA to Combined-Cycle

NGA to Combustion Turbine

NGA to Steam Electric

Coal to IGCC-CCS

Coal to IGCC-CCS Retro

Coal to IGCC

Coal to Oxfuel-CCS

Coal to Supercritical-CCS Retro

Coal to Supercritical Steam

Coal to New Steam-CCS Retro

Coal to New Steam

Coal to Existing Steam-CCS Retro

Coal to Existing Steam

Nuclear

NGCC

Supercritical

Coal - Existing

Hydro


28/65

4.1.2. L1 Mitigation Scenario

While numerous emissions scenarios were investigated, the body of this report will focus on theL1 scenario for deeper analysis, since it is closest to the Waxman-Markey bill that is closest tobeing implemented. Once the CO2 emissions targets under the L1 scenario were incorporated intothe model, a very different technological landscape in the energy sector appears. Figure 4.3

reveals that the electricity sector bears the brunt of economy-wide emissions reductions.Emissions from that sector decline steadily over time, while the level of sequestered CO2 fromCCS-equipped units increases. This trend requires that electricity sector emissions be reduced tonet negative values by 2035 given that emissions are nearly zero with a large amount of CCStaking place. The transportation sector is the only other sector with an appreciable emissionsreduction. This alteration comes from the release of hydrogen-powered cars, as discussed inSection 5.5. Hydrogen production facilities incorporated with CCS spearhead this reduction. Thefact that this is the only major source of emissions reductions outside of electricity productionillustrates a shortcoming of MARKALs reliance on inelastic demand. Under this representationof demand, even if it costs 100 times as much as it used to, consumers are still going to drive thesame amount, cook the same amount, and use the same amount of space heating. This trend isexaggerated even further in 2050 once the electricity sector has done nearly all that it can to

reduce emissions, the implied carbon price skyrockets over $25,000. This finding illustrates thatin order to meet stringent emissions targets, demand-side reductions must occur in parallel withsupply-side reductions. Although results from having inelastic demand are useful in determiningupper bounds of technological deployment and offer insight into tradeoffs between supply-sideoptions, the lack of detailed representation of consumer behavior limits the models usefulness asa forecasting tool.

Figure 4.3: System-wide and sectoral CO2 emissions for the L1 scenario

Given inelastic demand in the model, it is important to note that the electricity demand indicatedin Figure 4.4 is likely an upper bound on technological deployment. It should also be noted thatthis generation mix is the optimal solution only through the model time horizon of 2050 and doesnot consider meeting emissions requirements beyond that point. It is surprising to note that, even

-4,000

-2,000

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2000 2010 2020 2030 2040 2050

Carbon

Diox

ide

Em

iss

ions

(MMTCO

2)

Electricity Production

Industry

Commercial

Residential

Transportation

After CO2 intensityreductionAfter efficiencyimprovementsConstant techs andfuelsCapture & Storage

Biomass


29/65

though emissions from the electricity sector are reduced below zero by 2050, electricity demandin 2050 increases by more than 25 percent from the base case.

It is clear from Figure 4.4 that the technological makeup of electricity generation is very differentin a carbon-constrained economy. The starkest contrast is the large expansion of wind power,which expands to roughly half of the generation share by 2050. Caveats and discussion of this

result is discussed in more detail in Section 5.1. The other largest technological expansion isnuclear, both in conventional and advanced reactors. Advanced reactors provide inherently saferreactors at higher efficiencies and lower capital costs (e.g., high-temperature gas-cooled reactors).These advanced reactor designs are constrained by an upper bound on deployment in thisscenario. This constraint allows the model to keep deployment rates in check as well as to ensurethat a low-cost, low-carbon technology does not dominate the technological landscape. The largedeployment of nuclear reactors will be discussed in more detail in Section 5.2.

Coal use has a binding lower bound constraint placed on it through 2020, which represents itsentrenchment in the US electricity sector and allows a decade to move away from the carbon-intensive energy resource. This constraint forces the early deployment of IGCC with CCS in 2015and 2020. These installments remain in use through 2045 and by 2030 make up the entirety of

coal use for electricity. NGCC plants see very limited expansion beyond 2005 levels, with newplants coming online in later years with CCS incorporated.

Figure 4.4: Electricity production by technology under L1 scenario

While the model is constrained to meet emissions targets, a shadow price for CO2 exists thatrepresents the implied carbon price when the LP finds an optimal solution subject to the bindingemissions constraints. Figure 4.5 illustrates that the carbon price is on the order of $100/t-CO2 formost of the timeline until later years where, as previously mentioned, it increases dramatically.This carbon price motivates all supply-side changes and likely would have a large effect on


30/65

demand-side changes if these could be incorporated in the model. The large jump in price from2035 forward influences the expansion of wind, as more costly wind resources are deployed.

Figure 6: Implied carbon price under L1 scenario with different model configurations

4.2. Enhanced CCS ModelOnce the additions discussed in Chapter 3 are incorporated into the database, the L1 scenarioagain constrains the updated model. While a similar picture of emissions reductions by sector wasfound, a different set of technologies were used to meet electricity demand. The electricitygeneration landscape in Figure 4.6 remains largely unchanged as well, except in the case of coaluse. A large amount of retrofits to existing coal plants take place in 2015 with the enhanced CCSmodel, while a similar amount of new CCS was still installed. Oxy-fuel combustion with CCSshared some of the deployment with IGCC with CCS by 2020; however, the cost andperformance parameters two technologies were equated in the model and represent the same CCSdeployment. Previously, IGCC with CCS dominated the deployment of CCS-equippedtechnologies, as it is made available at an earlier time to meet demand. This large deployment ofCCS in both new and retrofit forms was in response to decreased storage costs from a flat rate to

a step function representing value-added storage options. There is no deployment of capture readyor partial capture plants by the model after their incorporation into the mix of technologies, whichis discussed further in Sections 5.3 and 5.4.

$-

$50

$100

$150

$200

$250

$300

2000 2010 2020 2030 2040 2050

Carbon

Price

($/ton

CO

2)

Enhanced Base Full


31/65

Figure 4.6: Electricity generation by technology under enhanced CCS model with L1 scenario

Given this expansion of CCS technologies, it is interesting to consider the effect this deploymenthas on the amount of CO2 sequestered. Figure 4.7 illustrates both the increase in CCS marketpenetration as well as the levels of CO2 sequestered by the different models. There is a clearincrease in CCS market penetration as a result of the new CCS modeling credited almost solely toretrofits to existing plants. The chart on the bottom ofFigure 4.7 shows that all of the value-addedstorage in each time period is filled and the storage with no storage costs is filled in many of thetime periods. CCS expands in later years to begin filling more costly storage options.

0

200

400

600

800

1,000

1,200

2000 2010 2020 2030 2040 2050

Elec

tricity

Pr

oduc

tion

(Tri

llion

kWh)

Retrofit

New CCS


32/65

Figure 4.7: Electricity production from new and retrofitted technologies (top), and total amount of CO2sequestered (bottom) in three different model configurations (base, enhanced, full enhancements in eachtime step)

The one model improvement not installed in the previous scenario was that of a cap on retrofitsavailable to existing coal plants of 60 GW. Once this constraint was added to the model,MARKAL demonstrated one of the less-desirable traits of a cost minimization model: the bang-bang solution. This provided enough of a tipping point to shift the technological makeup ofelectricity generation drastically within coal use. Figure 4.8 shows the shift of coal use fromIGCC-CCS to new SCPC plants. These lower cost plants allow for a shift in the early peak in the

implied carbon price from 2010 to 2020 in the fully enhanced model as shown in Figure 4.6.Further investigation reveals that this shift affects much of the energy market from 2010-2030.This enhanced model has slightly less electricity use in the residential sector, leading to lessoverall electricity demand. It also has much more biomass feedstock for industrial use reducingemissions from that sector. This new scenario also uses more efficient fuels and technologies thanthe previous case for 2010-2030. There are much higher electricity prices in this full model in thenear term to spur these efficiency switches. This scenario has much the same technologicalmakeup as the previous analysis from 2030 forward.

0

200

400

600

800

1,000

1,200

1,400

2020 2030 2040 2050

Seques

tere

dCO2

(MMTCO

2)

Base New Full

-$7/ton CO2

$0/ton CO2


33/65

Figure 4.8: Electricity generation by technology in fully enhanced CCS model - L1 scenario

4.3. Technological and Policy Uncertainty Cases

One of the key motivators for this research is the need to understand how CCS development anddeployment will interact with pervasive uncertainties in policy and technology development. This

section shows a few of the scenario analyses conducted in this research that investigate some ofthe most critical questions related to technological and policy uncertainty. Although we exploredmore scenarios than those shown in this section, the scenarios shown below are the mostimportant and interesting cases analyzed.

As Section 4.1 illustrated, the L1 policy constraint leads to large contributions from wind by2050, where it comprises over 50 percent of the electricity generation share. However, thisdeployment assumes the availability of energy storage technologies (e.g., flow batteries orcompressed air storage) and investments in load management technologies. In order to determinehow technology development delays for intermittent and smart grid technologies impact CCS, wecreated an Intermittent Technology Uncertainty scenario. This scenario limits the maximumdeployment of intermittent technologies (including wind and solar resources) to a cap of 20

percent of the electricity generation for any time period, assuming that energy storagetechnologies are delayed.

Figure 4.9 illustrates the generation shares for major electricity technologies under thisintermittent constraint. In order to meet emissions caps, more CCS is deployed compared with theL1 scenario without this constraint, in which wind plays a dominant role as an abatementresource. It is important to note that CCS does not enter the technology mix until 2030 when the

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2000 2010 2020 2030 2040 2050

Electric

ityGenera

tion

(Tri

llion

kW

h)

NGCCNuclear

Adv. Nuclear

Wind

IGCC-CCSCoal - ExistingSupercritical Retrofits -SCPC


34/65

constraint becomes binding (in this case, wind deployment reaches the maximum generation levelfor intermittents) in the model.

In the limited portfolio case without large-scale availability of energy storage to facilitate thedeployment of wind and solar, marginal CO2 abatement costs are higher than the case where thefull portfolio of mitigation options are available. Figure 4.10 shows the implied CO2 (shadow)

price in these two scenarios. As expected, CO2 shadow prices are a few percentage points higherfor the limited portfolio in the early years, but become significantly higher toward the end of thetime horizon. This effect suggests that the abatement cost benefit of wind is most pronounced ashigh penetration of intermittents could be facilitated by the presence of energy storage.

Figure 4.9: Electricity generation by technology for limited intermittent technology uncertainty case

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2000 2010 2020 2030 2040 2050

Elec

tricity

Genera

tion

(Trillion

kWh)

Solar PV

Wind

Adv. nuclear

NuclearNGCC

Coal Existing

Supercritical

IGCC-CCS

Oxy-fuelRetrofits Existing


35/65

Figure 4.10: Shadow price for CO2

for limited intermittent technology uncertainty case

In addition to wind, the analyses in Section 4.1 suggest that new nuclear plants also will have aconsiderable role in meeting stringent emissions caps. Model results suggest that advancednuclear plants like pebble-bed modular reactors and new light water reactor designs will play animportant mitigation role. However, barriers of political acceptance, waste storage, and potentialcost escalations for raw materials may limit the deployment of new nuclear capacity as discussedin more detail in Section 5.2. In the New Nuclear Technology Uncertainty scenario, existingnuclear facilities are allowed to operate until retirement but no new nuclear facilities can bepermitted or built until 2050.

Figure 4.11 suggests that, in the absence of new nuclear generation, wind expands to over 50percent of the portfolio mix to make up for the lack of nuclear. Additionally, centralized solarthermal plants begin deployment more widely. However, it is important to note that generationfrom CCS-equipped plants only increases slightly in this scenario. Less CCS growth comparedwith the limited intermitted case can be attributed to the fact that low wind costs make it adominant mitigation technology in the model. Since wind has greater deployment than advancednuclear under the full portfolio L1 scenario, placing a constraint on wind will leave more roomfor CCS to gain a greater share of the generation portfolio mix than adding restrictions to nuclear.As in the limited intermittent case, Figure 4.12 shows that a limited portfolio of technologieswithout the possibility of deploying new nuclear before 2050 would raise compliance costs formeeting CO2 caps.

$0

$50

$100

$150

$200

$250

$300

$350

2015 2020 2025 2030 2035 2040 2045 2050

CO

2Sha

dow

Price

($/t-C

O2

)

Full Portfolio 20% Intermittent Cap


36/65

Figure 4.11: Electricity generation by technology for new nuclear technology uncertainty case

Figure 4.12: Shadow price for CO2 for no new nuclea

markal and the role of ccs in greenhouse gas mitigation scenarios

Documents