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Policy Analysis

The Economic Merits of Flexible Carbon Capture and Sequestration as a Compliance Strategy with the Clean Power Plan Michael T. Craig, Paulina Jaramillo, Haibo Zhai, and Kelly Klima Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b03652 • Publication Date (Web): 21 Dec 2016 Downloaded from http://pubs.acs.org on January 2, 2017

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Environmental Science & Technology

The Economic Merits of Flexible Carbon Capture and Sequestration as a Compliance Strategy with the Clean Power Plan

Michael T. Craig1,*, Paulina Jaramillo1, Haibo Zhai1, Kelly Klima1

1

Department of Engineering and Public Policy, Carnegie Mellon University, 129 Baker Hall,

Pittsburgh, PA, 15213

*Corresponding author: E-mail: [email protected]. Address: Department of Engineering and Public Policy, Carnegie Mellon University, 129 Baker Hall, Pittsburgh, PA, 15213. Phone: 717-979-1463. Fax: 412-268-3757.

Supporting Information. Model formulation, additional method and data details, and supplemental results.

TOC/Abstract Art:

ACS Paragon Plus Environment


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ABSTRACT

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Carbon capture and sequestration (CCS) may be a key technology for achieving large CO2

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emission reductions. Relative to “normal” CCS, “flexible” CCS retrofits include solvent storage

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that allows the generator to temporarily reduce the CCS parasitic load and increase the

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generator’s net efficiency, capacity, and ramp rate. Due to this flexibility, flexible CCS

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generators provide system benefits that normal CCS generators do not, which could make

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flexible CCS an economic CO2 emission reduction strategy. Here, we estimate the system-level

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cost-effectiveness of reducing CO2 emissions with flexible CCS compared to re-dispatching (i.e.,

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substituting gas- for coal-fired electricity generation), wind, and normal CCS under the Clean

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Power Plan (CPP) and a hypothetical more stringent CO2 emission reduction target (“stronger

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CPP”). Using a unit commitment and economic dispatch model, we find flexible CCS achieves

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more cost-effective emission reductions than normal CCS under both reduction targets,

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indicating that policies that promote CCS should encourage flexible CCS. However, flexible

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CCS is less cost-effective than wind under both reduction targets, and less and more cost-

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effective than re-dispatching under the CPP and stronger CPP, respectively. Thus, CCS will

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likely be a minor CPP compliance strategy, but may play a larger role under a stronger emission

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reduction target.

18 19 20

INTRODUCTION Climate change poses a serious threat to humans and natural systems.1 In order to avert

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severe climate change, carbon dioxide (CO2) emissions from the electric power sector must

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drastically decrease.2 Many studies suggest that large (>50%) CO2 emission reductions will not

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be possible without carbon capture and sequestration (CCS).3,4 Yet, only one utility-scale power


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plant equipped with CCS – the Boundary Dam plant – is currently operational worldwide.5 The

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Boundary Dam plant is equipped with amine-based post-combustion CCS, the most

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commercially-developed CCS system, which uses liquid amine to absorb and remove CO2 from

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the flue gas of a coal-fired electric generating unit (EGU). “Normal” CCS retrofits like the one at

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the Boundary Dam plant can reduce CO2 emissions but consume substantial amounts of energy

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due to large parasitic loads of the CO2 capture process, which in turn substantially reduces the

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net power capacity of the retrofitted EGU. Large retrofit capital costs and increased operation

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costs associated with the large parasitic loads have hindered large-scale CCS deployment.6

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Relative to a normal CCS retrofit, a “flexible” CCS retrofit includes an additional feature

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– solvent storage – that allows the generator to eliminate most of the large parasitic loads of the

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CO2 capture process while maintaining a constant CO2 capture rate for up to several hours,

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depending on solvent storage capacity.7,8 This temporary reduction in the large parasitic loads

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allows a flexible CCS generator to temporarily increase its net capacity, net efficiency, and

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ramping capability,7,9 which in turn yields system benefits that are not available in a normal CCS

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generator. Furthermore, this flexibility may be increasingly valuable as the penetration of

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renewables and other technologies increases.10 Because of these system benefits, flexible CCS

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may be an economic strategy to reduce CO2 emissions. Flexible CCS retrofits can achieve

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similar benefits by venting flue gas,7,9 but venting reduces the unit’s CO2 capture rate. Here, we

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focus on flexible CCS as a CO2 emission reduction strategy, so only assess flexible CCS retrofits

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with solvent storage.

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Prior research on flexible CCS with solvent storage can largely be divided into two

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groups. One group used profit-maximizing optimization models with exogenous electricity

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prices to determine the optimal operations and profitability of flexible CCS generators.7,8,11



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These papers demonstrated that flexible CCS units could use solvent storage to shift the parasitic

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load of the CO2 capture process from high to low electricity price periods, thereby arbitraging

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electricity price variability throughout the day. However, this arbitrage only marginally increased

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profits in systems with very large intra-day price differentials.11 Furthermore, adding solvent

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storage to a normal CCS generator tended to only increase the profitability of a CCS plant at low

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carbon prices, when construction of a CCS generator would not be justified.7,8,12

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Other research used cost-minimizing dispatch models that demonstrated system-wide

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benefits of flexible CCS generators. Van der Wijk et al. (2014),9 for instance, found that solvent

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storage-equipped flexible CCS generators could provide four to ten times greater amounts of up

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reserves (including frequency regulation, contingency, and wind power forecast error balancing

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reserves) than normal CCS generators, which would reduce reserve provision costs. They also

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demonstrated that flexible CCS could provide slight reductions in system emissions and wind

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curtailment compared to normal CCS. Conversely, Cohen et al. (2013)13 demonstrated benefits

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from increased reserve provision by venting-equipped flexible CCS generators but not from

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adding solvent storage to those generators.

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Taken together, past research suggests that a weak case exists for private investment in

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flexible CCS, but system benefits of flexible CCS may make it an economic CO2 emission

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reduction strategy. Yet, past papers did not compare flexible CCS to common CO2 emission

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reductions strategies like re-dispatching (decreasing the capacity factors of coal-fired EGUs by

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increasing the capacity factors of less carbon-intensive EGUs, mainly natural gas) and building

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new wind capacity. Furthermore, neither Cohen et al. (2013)13 nor Van der Wijk et al. (2014)9

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included reserve provision costs in the optimization problem, which is necessary to fully value


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solvent storage. Thus, little information exists on the trade-offs between using flexible CCS and

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other strategies for reducing CO2 emissions.

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Here, we begin to fill these knowledge gaps by considering flexible CCS as an emission

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reduction technology in the context of the Clean Power Plan (CPP) and a hypothetical “stronger

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CPP”. The CPP set the first federal limits on CO2 emissions from existing EGUs in the United

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States and aims to reduce CO2 emissions in 2030 by 870 million short tons, or 32% from 2005

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levels.14 This target is meaningful but likely insufficient to meet climate stabilization goals.15

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Thus, we also consider a hypothetical stronger CPP that would require a 50% reduction in

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existing EGU emissions (relative to 2005 levels) by 2030, using the same emission reduction

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framework as the CPP.

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We assess the merits of flexible CCS as a CPP or stronger CPP compliance strategy with

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two metrics. First, we compare the cost-effectiveness of CO2 emission reductions of flexible

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CCS retrofits to other CPP compliance strategies, namely re-dispatching, additional wind, and

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normal CCS retrofits. In this context, we define cost effectiveness as the total operational and

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capital costs per ton of CO2 emissions reduced. Second, we calculate the equivalent capital cost

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(ECC) for flexible CCS retrofits relative to each of the alternative compliance strategies

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(individually, not in aggregate), which indicates the capital cost of flexible CCS at which it

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would reach the same cost-effectiveness and quantity of CO2 emission reductions as an

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alternative compliance strategy.

88 89 90 91

METHODS The upper Midwest area of the Midcontinent Independent System Operator (MISO), which oversees a competitive wholesale market place, will likely face a large capacity of coal-



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fired plant retirements16 and has good wind resources,17 making it an ideal study system for

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changes under the CPP. As such, we conduct our study in this region, which includes North

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Dakota, South Dakota, Minnesota, Iowa, Wisconsin, Michigan, Missouri, Illinois, and Indiana.

95 96

Unit Commitment and Economic Dispatch (UCED) Model

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We determine operational costs and emissions of each power plant fleet using a UCED

98

model that minimizes total system electricity, reserve, start-up, and non-served energy costs

99

subject to various system- and unit-level constraints. By including reserve costs in the objective

100

function of our UCED, we capture the changes in operating costs that result from flexible CCS

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generators providing system reserves. We constructed the UCED model in PLEXOS Version

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7.2,18 a commercially-available software package commonly used in power system analyses,19

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and solved it using CPLEX Version 12.6.1.20 We ignore transmission constraints within MISO

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and imports and exports to and from MISO to limit problem size.21 The Supporting Information

105

(SI) provides the complete UCED formulation and the 2030 demand profile used in the UCED.

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The UCED model runs at hourly intervals for a 24-hour period, like MISO’s day-ahead

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market,22 plus an additional 24-hour look-ahead period in 6-hour intervals. The look-ahead

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period allows us to optimize dispatch decisions over a longer time horizon and capture the value

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of inter-day solvent storage. After each optimization, the UCED model steps forward 24 hours

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and uses the prior solution as initial conditions for the subsequent optimization. Like other

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UCED models,9,21,23 we assume perfect wind, solar, and demand forecasts, so do not dispatch

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generators in a real-time market subsequent to the day-ahead UCED solution. While this may

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bias our results in favor of wind power, Rahmani et al. (2016)24 find that including a real-time

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market within a day-ahead UCED model increases system costs and CO2 emissions by less than


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1%. Thus, we expect any bias to be small and to not qualitatively change our results.

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Furthermore, to indirectly account for wind forecast error and the value of flexible CCS in

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supporting wind integration, we set hourly spinning reserve requirements to 3% of maximum

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daily load plus 5% of hourly wind generation.21,25 Additionally, the UCED model allows for

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curtailment of wind and solar generators by including them as dispatchable resources with hourly

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capacity factors (SI).26,27

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In order to include reserve costs in the UCED objective function, a cost coefficient, or

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reserve offer price, must be included with each generator’s reserve offers. To determine this

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coefficient, we assume that reserve offer prices are proportional to the generator’s operating cost,

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or marginal cost of energy.28,29 Based on 2015 MISO energy30 and spinning reserve offer

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prices,31 the capacity-weighted average proportion of spinning reserve to energy offer prices is

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approximately 26%. As a result, we set spinning reserve offer prices to 26% of each generator’s

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operating cost.

128 129

Base Generator Fleet

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We construct a base generator fleet for 2030 for the upper Midwest portion of MISO. The

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base fleet accounts for fleet changes through 2030, such as generator additions and retirements,

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expected under the CPP. However, generator additions and retirements characterized in this base

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fleet are not sufficient to comply with the CPP, as CPP compliance will be largely driven by re-

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dispatching, i.e. scheduling lower-emitting plants to generate more electricity than they would in

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the absence of the CPP. As a result, while our base fleet accounts for power plant additions and

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retirements (some of which may be driven by the CPP), we define our compliance scenarios as

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the additional strategies that would enable the base fleet to comply with the CPP. Such strategies



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include re-dispatching, adding wind capacity, and/or adding normal or flexible CCS retrofits to

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this 2030 base fleet.

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It could be argued that adding our compliance strategies to a 2030 base fleet instead of a

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2015 fleet may underestimate the cost-effectiveness of these strategies because CCS could

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replace some of the fleet changes we assume will occur between now and 2030 as states comply

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with the CPP. We suggest, however, that many of these fleet changes through 2030, particularly

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coal plant retirements and renewable capacity additions, would likely occur even without the

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CPP due to existing environmental regulations and state-level policies such as Renewable

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Portfolio Standards.32 Furthermore, since the Clean Energy Incentive Program14 under the CPP

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incentivizes early deployment of energy efficiency and renewables, these technologies will likely

148

be deployed prior to CCS. Thus, our analysis considers CCS as a post-2030 CPP compliance

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strategy, which provides sufficient time to plan for CCS deployment. Finally, by also analyzing

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larger emission reductions under a hypothetical stronger CPP with the same 2030 base fleet, we

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capture the effect on costs and emissions from each compliance strategy in a generator fleet that

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has not already changed in response to an emission reduction target, thereby eliminating any bias

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that may occur in our CPP analysis.

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To build our base fleet, we rely on a 2030 generator fleet33 from the EPA’s Integrated

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Planning Model (IPM), a cost-minimizing dispatch and capacity expansion optimization model

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for the U.S. electric power system that forecasts generator additions, retirements, control

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technology retrofits, and other changes in the power plant fleet in response to regulations.34 To

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compensate for omitted data and aggressive hydropower deployment, we alter the IPM fleet in

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several ways, including changing power plant heat rates and adding necessary unit commitment

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parameters like ramp rates, as further described in the SI. Our final base fleet consists of 1,232


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generators with a total capacity of 164 GW, including 50 GW coal-fired, 54 GW gas-fired, 33

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GW wind, and 18 GW nuclear capacity. The SI provides additional installed capacity details and

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fuel prices.

164 165

CPP Compliance Scenarios

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States can comply jointly with the CPP under a single rate- or mass-based target.14 Given

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that states have extensive experience with the SO2 cap-and-trade program,35 we assume that

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states in our study region will comply jointly with the CPP or stronger CPP under a single

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regional mass-based limit that equals the sum of each state’s mass limit. Under the CPP, the

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regional CO2 emissions mass limit equals 346 million tons in 2030, or 32% below 2005

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emissions.36 To test the sensitivity of our results to larger emission reductions, we also assess a

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hypothetical “stronger CPP”, under which the regional CO2 emissions mass limit equals 249

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million tons in 2030, or 50% below 2005 emissions. Since the EPA projects that re-dispatching

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among affected EGUs in combination with building additional wind power capacity will account

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for the bulk of emission reductions under the CPP,16 we include these as our first two strategies.

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Additionally, we include normal and flexible CCS retrofits.

177 178 179

Re-dispatching among Affected EGUs Enforcing re-dispatching to comply with the CPP through a mass limit in our UCED

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model would be intractable, as it would require running the UCED for an entire year at once.

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Instead, we enforce re-dispatching among affected EGUs by including a CO2 price on emissions

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from all affected EGUs as described in Oates and Jaramillo (2015).37 Note that we include this



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CO2 price not to analyze a carbon tax, but to achieve sufficient re-dispatching within the UCED

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model among affected EGUs to comply with the CPP. The SI describes our selection of affected

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EGUs. To calculate this CO2 price we use a simple economic dispatch (ED) model. The ED

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model, fully described in the SI, minimizes total energy costs subject to the constraints that

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supply equals net demand and each generator’s electricity generation varies between zero and its

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maximum capacity. Net demand equals demand minus wind and solar generation based on the

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same capacity factors as the UCED model.26,27 To determine the CO2 price, we increment a CO2

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price upwards from $0/ton in $1/ton increments until CO2 emissions from affected EGUs meet

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the regional mass limit in the simple ED. We then include these CO2 prices in the operating cost

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and reserve offer prices of affected EGUs in the full UCED.

193 194 195

Normal and Flexible CCS Retrofits To evaluate CCS as a compliance mechanism, we model CCS systems with a 90% CO2

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capture rate, which maximizes the efficiency of the CO2 removal process in a cost-effective

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manner.38 We select coal-fired generators for CCS retrofits based on four common attributes of

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coal-fired generators for which CCS retrofits are most economic: generators 1) younger than 40

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years old (as of 2020), 2) with net thermal efficiencies greater than 30%, 3) with net capacities

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greater than 300 MW, and 4) with SO2 scrubbers and selective catalytic reduction (SCR) for

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post-combustion NOx control.39 From this group of eligible generators, we retrofit CCS on

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generators in order of decreasing net efficiency prior to the CCS retrofit because more efficient

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generators are more likely to be economically viable to operate, and therefore profitable, post-

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CCS retrofit.


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In order to examine how the cost-effectiveness of CO2 emission reductions changes with

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increasing CCS deployment, we construct three normal and three flexible CCS compliance

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scenarios for the CPP and stronger CPP each (for a total of 12 CCS scenarios) by modeling CCS

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retrofits on coal-fired generators. To determine the retrofit CCS capacity in these scenarios, we

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first assume all eligible coal-fired generators (per the four above criteria) retrofit CCS, then

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assume two lower capacities of CCS retrofits. This process yields combined net CCS capacities

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of 2, 4.5, and 8.5 GW. After accounting for the net capacity penalty of CCS retrofits, the de-rated

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CCS-equipped coal-fired capacities in these scenarios are 1.6, 3.9, and 6.2 GW, respectively, or

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up to 4% of the total installed capacity of the base fleet. With 6.2 GW of CCS-equipped

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generators, the scenario complies with the CPP without a CO2 price. However, the other two

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scenarios require some re-dispatching in addition to the CCS retrofits to comply with the CPP, so

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we also enforce re-dispatching in those scenarios via a CO2 price determined with the simple ED

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model. The same CCS installed capacities are also used in compliance scenarios with our

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hypothetical stronger CPP, as no additional coal-fired generators meet the four above criteria for

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CCS retrofits.

220 221 222

Additional Wind Capacity In order to model compliance using wind power instead of CCS, we create three wind

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compliance scenarios under the CPP and under the stronger CPP. To create each wind scenario,

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we add wind capacity to the 2030 base fleet until the scenario’s CO2 price necessary to comply

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with the CPP as calculated with the simple ED model equals that of a CCS scenario. By

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controlling for CO2 price between the CCS and wind compliance scenarios, we hold constant the

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effects of re-dispatching on emissions and costs, which allows for a direct comparison between



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additional wind and normal and flexible CCS retrofits as compliance strategies. Relative to the

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base fleet, which already includes 33 GW of installed wind capacity, this results in 2.5, 5.5, and

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6.5 GW of additional wind power capacity under the CPP and 3, 9, and 14 GW of additional

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wind power capacity under the hypothetical stronger CPP. The SI provides the CO2 price

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included in each compliance scenario.

233 234

Normal and Flexible CCS Models

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In order to include CCS in our UCED model, we need operating parameters for

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retrofitted coal-fired generators. For this analysis, we assume the maximum fuel input to the

237

boiler at a coal-fired generator remains constant before and after the CCS retrofit, and that no

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auxiliary boilers are installed. As such, the coal-fired generator must provide the entire parasitic

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load of the CCS system, meaning its net efficiency and capacity, and therefore ramp rate,

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decrease upon CCS retrofit. Given elevated SO2 removal requirements of CCS, we also zero out

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SO2 emissions upon CCS retrofit. To estimate generator-specific CCS retrofit parameters, e.g.

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net capacity and heat rate penalties, we rely on linear regressions based on data from the

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Integrated Environmental Control Model (IECM), a power plant modeling tool,40 as detailed in

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Craig et al.41 We use net heat rate as the independent parameter for these regressions and develop

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separate regression models for bituminous and sub-bituminous coal.

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A coal plant with flexible CCS has additional specific operating constraints compared to

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normal CCS. To account for these operating constraints, we develop a flexible CCS operational

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model that provides constraints that can be included in the UCED model and estimates

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operational costs and emissions across flexible CCS operations. This flexible CCS model

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disaggregates a single flexible CCS generator into eight separate proxy units that account for net


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electricity generation, costs, and emissions of the flexible CCS generator in different operational

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modes, e.g. while discharging stored lean solvent. Proxy unit operations are linked through

253

numerous constraints. To estimate flexible CCS design and operational parameters, we use a

254

literature review and regressions based on IECM data. Table 1 summarizes key normal and

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flexible CCS parameters. The SI further details these and other CCS parameters and Craig et al.41

256

provide a complete description of our flexible CCS model.

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Table 1: Key normal and flexible CCS design and operational parameters. See the SI for

258

additional details and Craig et al.41 for a full description of our flexible CCS model. Normal or Flexible CCS Parameter

Value

Normal CCS retrofit efficiency penalty

32-46%

Normal CCS retrofit capacity penalty

24-32%

Normal CCS generator ramp rate

1.5-2.9% of capacity/min.

Flexible CCS generator max reduction in CCS retrofit parasitic

90%

load while discharging stored lean solvent Flexible CCS generator ramp rate while discharging stored lean

4% of capacity/min.

solvent Flexible CCS solvent storage capacity (i.e., max hours of peak

2 hours

power output enabled by discharging stored lean solvent) 259 260

Cost-Effectiveness and Equivalent Capital Cost Calculations

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The results of the UCED model provide the basis for comparing operational costs and

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benefits of our compliance scenarios. However, that model does not account for capital costs of

263

new wind installations or CCS retrofits. In order to compare the effectiveness of our compliance



264

scenarios, a consistent metric must be used that accounts for all costs and CO2 emissions

265

benefits. We therefore calculate the cost-effectiveness (CE) of reducing CO2 emissions for each

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compliance scenario (c) as: 𝐶𝐸𝑐 =

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(𝑇𝑂𝐶𝑐 +𝑇𝐶𝐶𝑐 )−𝑇𝑂𝐶𝐵𝑎𝑠𝑒

(Equation 1)

𝐴𝐶𝐸𝑐 −𝐴𝐶𝐸𝐵𝑎𝑠𝑒

268

where Base refers to the base fleet; TOC = total operational costs, or the sum of electricity

269

generation, start-up, and reserve costs [$2011]; TCC = total annualized capital costs of additional

270

wind capacity or CCS retrofits added in our compliance scenarios [$2011]; and ACE = total CO2

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emissions from affected EGUs [tons]. The SI specifies how we calculate TOC and the capital

272

recovery factor (CRF) we use to annualize capital costs. We assume revenues collected through a

273

carbon market are recycled into the electric power sector, so they do not constitute real economic

274

costs and are not included in TOC. To account for uncertainty in capital costs of wind and CCS

275

retrofits, we calculate cost-effectiveness over a range of capital costs (SI).

276

With these cost-effectiveness estimates, we calculate a per-kW equivalent capital cost

277

(ECC) for flexible CCS relative to each compliance strategy. Each ECC indicates the capital cost

278

of flexible CCS retrofits at which such retrofits would reach the same cost-effectiveness and

279

quantity of CO2 emission reductions as an alternative compliance strategy. We calculate the ECC

280

as: 𝐸𝐶𝐶𝑓,𝑎 =

281

(𝐶𝐸𝑎 ∗𝐴𝐶𝐸𝑎 )−𝑇𝑂𝐶𝑓 𝐹𝐶𝐶𝑓

1

∗ 𝐶𝑅𝐹

𝑓

(Equation 2)

282

where f and a indicate flexible CCS and alternative compliance strategies, respectively, and FCC

283

= installed de-rated capacity of flexible CCS [MW]. We use the same CRF as Equation 1.

284 285

RESULTS


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CO2 Emission Reductions in Compliance Scenarios

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Figure 1 highlights that the base 2030 fleet does not, on its own, comply with either

288

policy, but its CO2 emissions are much closer to the CPP than stronger CPP mass limit.

289

Consequently, CO2 emission reductions relative to the base fleet under the stronger CPP

290

compliance scenarios are five to seven times greater than those under the CPP compliance

291

scenarios. For instance, the re-dispatch scenario reduces CO2 emissions relative to the base fleet

292

by 20 and 120 million short tons under the CPP and stronger CPP, respectively, a six-fold

293

difference. Due to the large difference in CO2 emission reductions necessary to comply with the

294

CPP and stronger CPP, compliance scenarios’ generation mixes differ substantially between the

295

two reduction targets. Most notably, coal-fired generation, which provides 50% of electricity

296

generation in the base fleet, declines by 5–10% and 38–45% in compliance scenarios under the

297

CPP and stronger CPP, respectively. The SI provides the generation mix for each compliance

298

scenario.

299

Since we set the CO2 price for each compliance scenario using a simple ED model that

300

does not account for all system constraints, that price does not guarantee emission reductions in

301

the full UCED model. In fact, Figure 1 indicates that the scenario that reduces emissions solely

302

through re-dispatching does not comply with the CPP, whereas the compliance scenarios that

303

reduce emissions partly through additional wind or CCS retrofits do not comply with the stronger

304

CPP. However, these instances of non-compliance are a construct of using a CO2 price

305

determined by a simplified ED model to comply with the relevant emission mass limits, not an

306

indication that the strategy could not be used to comply with the mass limit. Indeed, affected

307

EGU CO2 emissions under these scenarios only exceed the relevant mass limit by less than 2.5%.



308

Thus, we subsequently analyze these scenarios in the same way as scenarios that comply with the

309

relevant mass limit.


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310 311 312 313

Figure 1: Total emissions from affected EGUs under the CPP (left) and stronger CPP (right) from our UCED model, plus each

314

emissions mass limit (black line). * indicates wind and CCS compliance scenarios that include some re-dispatching.

315



316

Total Costs

317

This section discusses the total annualized costs included in the cost-effectiveness

318

calculation for each compliance scenario (see numerator of Equation 1). Electricity generation

319

costs dominate total annualized costs under the CPP and stronger CPP. In the re-dispatch CPP

320

compliance scenario, for instance, annual electricity generation costs equal $13.2 billion ($2011),

321

whereas start-up and reserve costs equal $0.1 and $0.3 billion, respectively. Total annualized

322

costs also include annualized capital costs of new wind or CCS added in the compliance

323

scenarios, which account for 1% to 10% of total costs, depending on the scenario.

324

Table 2 provides total annualized cost increases relative to the base scenario for each CPP

325

and stronger CPP compliance scenario assuming best guess capital cost values. Among our CPP

326

compliance scenarios, total annualized costs relative to the base scenario increase between $100

327

million in the re-dispatch scenario and $1.28 billion in the 6.2 GW normal CCS scenario. Given

328

that re-dispatching increases costs the least, substituting re-dispatching with additional wind or

329

CCS increases total annualized costs. Total costs increase less under wind, which provides zero

330

marginal cost electricity, than flexible CCS. Flexible CCS, in turn, provides greater reserves than

331

normal CCS and therefore further reduces reserve costs,41 resulting in lower total annualized

332

costs than normal CCS. However, since reserve costs account for only a small fraction of total

333

annualized costs, the latter costs are not substantially lower with flexible than normal CCS.

334

Under the stronger CPP, re-dispatch costs increase relative to the CPP as electricity

335

generation increasingly shifts to units with lower CO2 emissions rates but higher operational

336

costs. Unlike under the CPP, costs increase the most under re-dispatching, and substituting re-

337

dispatching with wind or normal or flexible CCS reduces total annualized costs. For the same


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338

reasons as under the CPP, additional wind incurs the lowest cost increases, and flexible CCS

339

increases costs less than normal CCS.

340

Total annualized costs increase with increasing CCS penetration under the CPP but not

341

under the stronger CPP (Table 2) partly due to changing utilization of CCS-equipped generators

342

(SI). Under the CPP, the aggregate capacity factor for CCS-equipped generators equals 75% in

343

the 1.6 GW scenarios, but decreases to 40% in the 6.2 GW scenarios. Declining utilization plus

344

increasing CCS capital costs cause total annualized costs to increase with CCS penetration.

345

Higher capacity factors (by 10%) for flexible than normal CCS generators in the 3.9 and 6.2 GW

346

scenarios do not reverse this trend. Conversely, under the stronger CPP, aggregate capacity

347

factors for CCS-equipped generators equal roughly 100% in all scenarios.

348 349

Table 2: Incremental total annualized costs of each compliance scenario under the CPP and

350

stronger CPP relative to the base scenario assuming best guess capital cost values. Total Annualized Compliance Cost (million $2011) Compliance Scenario

CPP

Stronger CPP

Re-dispatch

100

2,770

Wind, 2.5 GW†

140

N/A

Wind, 5.5 GW†

230

N/A

Wind, 6.5 GW

270

N/A

Wind, 3 GW*

N/A

2,320

Wind, 9 GW*

N/A

1,840

Wind, 14 GW*

N/A

1,580

Normal CCS retrofits, 1.6 GW†*

350

2,580



Normal CCS retrofits, 3.9 GW†*

840

2,450

Normal CCS retrofits, 6.2 GW*

1,280

2,490

Flexible CCS retrofits, 1.6 GW†*

330

2,560

Flexible CCS retrofits, 3.9 GW†*

780

2,410

Flexible CCS retrofits, 6.2 GW*

1,180

2,400

351

†

352

CPP and stronger CPP, respectively.

and * indicate wind and CCS compliance scenarios that include some re-dispatching under the

353 354

Cost-Effectiveness of CO2 Emission Reductions

355

Cost-Effectiveness under the CPP

356

Our results indicate that the cost of complying with the CPP varies among compliance

357

strategies from $0 to $60 per ton of avoided CO2 (Figure 2). Depending on wind capital costs, re-

358

dispatching or wind achieves the most cost-effective CO2 emission reductions, whereas normal

359

CCS achieves the least cost-effective reductions. Additionally, replacing re-dispatching with

360

wind or CCS increases the quantity of emission reductions. Consequently, a trade-off exists

361

between cost-effectiveness and quantity of emission reductions when substituting CCS or wind

362

for re-dispatching, except at low wind capital costs (less than $1,650/kW). Among CCS

363

technologies, flexible CCS tends to achieve greater emission reductions more cost-effectively

364

than normal CCS, with the exception of the 1.6 GW CCS scenarios, which have low utilization

365

rates at one retrofit CCS plant.41

366

At each capacity of normal or flexible CCS retrofits, similar or greater reductions can be

367

obtained more cost-effectively with additional wind capacity. For instance, the 5.5 and 6.5 GW

368

wind scenarios achieve similar emission reductions as the 6.2 GW CCS scenarios, but at roughly


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369

a fifth of the cost. Additionally, while the 1.6 GW CCS scenarios can achieve as cost-effective

370

emission reductions as the 5.5 or 6.5 GW wind scenarios, the emission reductions they achieve

371

are roughly 13% lower.

372 373

Cost-Effectiveness under the Stronger CPP

374

Costs of compliance with the stronger CPP range from $7 to $23 per ton (Figure 2). Re-

375

dispatching achieves the most emission reductions but is the least cost-effective, as the addition

376

of wind or CCS increases cost-effectiveness (by up to 40%) and decreases emission reductions

377

(by up to 7%). This trend opposes that under the CPP, indicating the increasing cost-

378

effectiveness of CCS and wind relative to re-dispatching under a stronger emission reduction

379

target. Two factors mostly account for these opposing trends: higher re-dispatch costs and higher

380

capacity factors of CCS-equipped generators under the stronger CPP.41 Higher CCS capacity

381

factors yield greater system benefits for the same capital cost, thereby increasing the cost-

382

effectiveness of CCS.

383

Additional wind capacity tends to be a more economic compliance strategy than flexible

384

and normal CCS, as under the CPP. The 9 and 14 GW wind scenarios provide similar emission

385

reductions at greater cost-effectiveness (by 20–35%) than the 3.9 and 6.2 GW normal and

386

flexible CCS scenarios. All wind scenarios also achieve more cost-effective emission reductions

387

than the 1.6 GW CCS scenarios, but the 1.6 GW CCS scenarios achieve more emission

388

reductions.

389

Flexible CCS tends to be a more economic compliance strategy than normal CCS, as

390

under the CPP. At all tested capacities, flexible CCS achieves more cost-effective emission

391

reductions than normal CCS. Furthermore, at 1.6 and 3.9 GW of CCS retrofits, flexible CCS



392

achieves greater emission reductions than normal CCS. However, at 6.2 GW, flexible CCS

393

achieves less emission reductions due to less electricity generation by CCS-equipped

394

generators.41

395

The 3.9 and 6.2 GW CCS scenarios are the only two scenarios that are less cost-effective

396

under the CPP than stronger CPP for two reasons. First, utilization of CCS-equipped generators

397

decreases with increasing CCS penetration under the CPP (but not the stronger CPP), increasing

398

costs and reducing cost-effectiveness. Second, while CCS retrofits substitute for re-dispatching

399

under both targets, re-dispatching is a cost-effective CO2 emission reduction strategy under the

400

CPP but not the stronger CPP. Consequently, increasing CCS under the CPP reduces re-

401

dispatching and, in turn, cost-effectiveness. Indeed, in the 6.2 GW normal CCS scenario under

402

the CPP, no re-dispatching and low CCS capacity factors combine to create the least cost-

403

effective compliance scenario under both targets.


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404

405 406

Figure 2: Cost per ton of CO2 emission reductions versus CO2 emission reductions for each compliance scenario relative to the base

407

scenario under the CPP (left) and stronger CPP (right). Error bars indicate cost per ton at low and high capital cost values, dashed

408

vertical lines indicate the emission reductions necessary to achieve each mass limit, and * indicates wind and CCS compliance

409

scenarios that include some re-dispatching.



410

Flexible CCS ECCs

411

Relative to the wind compliance scenarios, flexible CCS ECCs are negative or below the

412

lowest flexible CCS capital cost estimate used in the cost-effectiveness analysis, or $1,170/kW,

413

under the CPP and stronger CPP (SI). Negative ECCs indicate that even if the capital cost of

414

flexible CCS was zero, flexible CCS would still be more expensive than the alternative

415

compliance strategy due to the energy penalty and consequently high operating cost of CCS.

416

ECCs relative to re-dispatching are also negative under the CPP, but range from $2,000–

417

$2,900/kW under the stronger CPP, above current estimates of flexible CCS capital costs (1,170–

418

1,490 $2011 per net kW), indicating that flexible CCS would likely be a more cost-effective

419

carbon mitigation strategy than re-dispatching under the stronger CPP. Across installed CCS

420

capacities under the CPP and stronger CPP, ECCs relative to normal CCS range from $1,300–

421

$1,600, some of which exceed the upper flexible CCS capital cost estimate ($1,490/kW), also

422

indicating that flexible CCS would likely be a more cost-effective carbon mitigation strategy

423

than normal CCS given best guess flexible CCS capital cost estimates.

424 425

DISCUSSION AND POLICY IMPLICATIONS

426

In order to better understand whether flexible CCS would be an economic strategy to

427

reduce CO2 emissions, we compared the cost-effectiveness of CO2 emission reductions with

428

flexible CCS to that of three alternative emission reduction strategies – re-dispatching, additional

429

wind capacity, and normal CCS retrofits – under the CPP and a hypothetical stronger CPP.

430

Under the CPP and stronger CPP, flexible CCS mostly achieved greater and more cost-effective

431

emission reductions than normal CCS. Additionally, in many scenarios, flexible CCS ECCs

432

relative to normal CCS exceeded high capital cost estimates currently available for flexible CCS.


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433

This finding in conjunction with the cost-effectiveness results indicate that flexible CCS, due to

434

its system benefits, would be a more economic CO2 reduction strategy than normal CCS from the

435

perspective of the power system. Thus, public policies aimed at encouraging CCS deployment

436

should prioritize support for flexible rather than normal CCS deployment.

437

However, under the CPP and stronger CPP we found that flexible CCS was a less

438

economic compliance strategy than wind, which achieved larger and more cost-effective CO2

439

emission reductions in most cases. Thus, under both reduction targets, wind would likely be a

440

more common compliance strategy than CCS. The comparison between re-dispatching and

441

flexible CCS is less clear. Under the CPP, re-dispatching achieved more cost-effective but less

442

emission reductions than flexible CCS, whereas the opposite was true under the stronger CPP.

443

As such, CCS and re-dispatching pose a trade-off between the cost and quantity of emission

444

reductions under both targets. However, the fact that CCS proved more cost-effective than re-

445

dispatching only under the stronger CPP suggests CCS would be a more viable compliance

446

strategy at higher emission reduction targets than those set forth under the CPP. Higher natural

447

gas prices would improve the merits of CCS relative to re-dispatching.

448

Nonetheless, given the existence of the CPP, our cost-effectiveness results indicate that

449

deployment of CCS in the mid-term in our study system, the upper Midwest, will likely be

450

limited, since at least one dominant emission reduction strategy (wind) exists. Furthermore, we

451

find flexible CCS ECCs relative to wind and re-dispatching are negative or below optimistic

452

capital cost estimates, indicating substantial capital cost reductions would be necessary for

453

flexible CCS to be an economically-competitive CO2 emission reduction strategy in the upper

454

Midwest. We expect similar results in other regions with good wind resources, moderate CO2

455

emission reduction targets, and/or large spare NGCC capacity, like Texas. In regions where these



456

conditions do not apply, e.g. the Mid-Atlantic, flexible CCS may be a cost-effective CO2

457

emission reduction strategy.

458

This research could be expanded in several ways. First, our UCED model runs in hourly

459

time steps, but shorter time steps may capture additional value from the flexibility of flexible

460

CCS generators and increase the value of flexible CCS relative to other compliance strategies.

461

Similarly, directly accounting for wind forecast error could increase the value of reserves and

462

flexible generation provided by flexible CCS. Additionally, we ignore transmission costs

463

associated with wind deployment, which could increase wind capital costs, or transportation and

464

storage costs and enhanced oil recovery revenues associated with CCS, which could increase or

465

decrease CCS costs. We also do not consider space limitations at coal-fired generators that could

466

preclude normal or, given larger space requirements for solvent storage tanks, flexible CCS

467

retrofits. Finally, flexible CCS may be more economic in systems with higher renewable

468

penetration. Whereas wind generates 18% of annual electricity in our test system (see SI),

469

California, for instance, has a mandate for 33% of electricity generation by 2020.42 Assessing the

470

value of flexible CCS in high renewable systems may yield markets suitable for initial flexible

471

CCS deployment.

472

The CPP is one of the main components of the U.S.’s Intended Nationally Determined

473

Contribution (INDC) submitted at the 2015 Conference of Parties 21 in Paris,43 but meeting the

474

targets set forth in the INDC will likely require further emission reductions than those that would

475

be achieved under the CPP15 or stronger CPP. Even if the CPP is strengthened, our analysis

476

indicates CCS will likely play a modest role in meeting the U.S. INDC. Yet, limiting temperature

477

rise to 2°C would require more aggressive emission reductions than those put forth by INDCs,44

478

and such reductions are likely not possible without CCS.3,4 Reconciling the poor mid-term


Page 26 of 36

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479

deployment prospects of CCS that we found with deployment needs to aggressively mitigate

480

climate change will likely require public funding or other support for CCS beyond the CPP in the

481

upper Midwest and potentially across the United States.

482 483

ACKNOWLEDGEMENTS

484

We thank the National Science Foundation (Grant Number EFRI-1441131), National Oceanic

485

and Atmospheric Administration (Award Number A14OAR4310249), Carnegie Mellon College

486

of Engineering, Steinbrenner Institute for Environmental Education and Research, and

487

Achievement Rewards for College Scientists (ARCS) Foundation for funding. The results and

488

conclusions of this paper are the sole responsibility of the authors and do not represent the views

489

of the funding sources. We thank Energy Exemplar for providing a free academic license for

490

PLEXOS.

491

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Base Redispatch Wind 2.5GW* Wind 5.5GW*

Scenarios

Wind 6.5GW Normal CCS 1.6GW* Normal CCS 3.9GW* Normal CCS 6.2GW Flexible CCS 1.6GW* Flexible CCS 3.9GW* Flexible CCS 6.2GW 200

250

300

350

Total CO 2 Emissions from Affected EGUs (million short tons) ACS Paragon Plus Environment

400


Page 34 of 36

Base Redispatch Wind 3GW* Wind 9GW*

Scenarios

Wind 14GW* Normal CCS 1.6GW* Normal CCS 3.9GW* Normal CCS 6.2GW* Flexible CCS 1.6GW* Flexible CCS 3.9GW* Flexible CCS 6.2GW* 200

250

300

350

Total CO 2 Emissions from Affected EGUs (million short tons) ACS Paragon Plus Environment

400

Page 35 of 36


Figure 2: Cost per ton of CO2 emission reductions versus CO2 emission reductions for each compliance scenario relative to the base scenario under the CPP (left) and stronger CPP (right). Error bars indicate cost per ton at low and high capital cost values, dashed vertical lines indicate the emission reductions necessary to achieve each mass limit, and * indicates wind and CCS compliance scenarios that include some redispatching. 248x175mm (120 x 120 DPI)



Figure 2: Cost per ton of CO2 emission reductions versus CO2 emission reductions for each compliance scenario relative to the base scenario under the CPP (left) and stronger CPP (right). Error bars indicate cost per ton at low and high capital cost values, dashed vertical lines indicate the emission reductions necessary to achieve each mass limit, and * indicates wind and CCS compliance scenarios that include some redispatching. 248x175mm (120 x 120 DPI)


Page 36 of 36

The Economic Merits of Flexible Carbon Capture ... - ACS Publications

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