Optimisation and evaluation of �exible operation

strategies for coal- and gas-CCS power stations with a

multi-period design approach

Evgenia Mechleria,b, Paul S. Fennellc, Niall Mac Dowella,b,∗

aCentre for Process Systems Engineering, Imperial College London, South Kensington,

London SW7 2AZ UKbCentre for Environmental Policy, Imperial College London, South Kensington, London

SW7 1NA UKcDepartment of Chemical Engineering, Imperial College London, South Kensington, UK

Abstract

Thermal power plants are increasingly required to balance power grids by com-

pensating for the intermittent electricity supply from renewable energy resources.

As CO2 capture and storage is integrated with both coal- and gas-�red power

plants, it is vital that the emission mitigation technology does not compromise

their ability to provide this high-value service. Therefore, developing optimal

process operation strategies is vital to maximise both the value provided by

and the pro�tability of these important assets. In this work, we present mod-

els of coal- and gas-�red power plants, integrated with a post-combustion CO2

capture process using a 30 wt% monoethanolamine (MEA) solvent. With the

aim to decoupling the power and capture plants in order to facilitate pro�t

maximising behaviour, a multi-period dynamic optimisation problem was for-

mulated and solved using these models. Four distinct scenarios were evaluated:

load following, solvent storage, exhaust gas by-pass and variable solvent regen-

eration (VSR). It was found that for both coal- and gas-�red power plants, the

VSR strategy is consistently the most pro�table option. The performance of the

exhaust by-pass scenario is a strong function of the carbon prices and is only

selected at very low carbon prices. The viability of the solvent storage strategy

∗Corresponding authorEmail address: niall@imperial.ac.uk (Niall Mac Dowell)

Preprint submitted to International Journal of Greenhouse Gas Control February 4, 2017

was found to be a strong function of the capital cost associated with the solvent

storage infrastructure. When the cost of the solvent tanks has been paid o�,

then the solvent storage scenario is 3.3% and 8% more pro�table than the base-

line for the pulverised coal and gas-�red power plants, respectively. Sensitivity

analyses showed that, for all strategies, the �exibility bene�t declined with re-

duced carbon and fuel prices, while a �peakier� electricity market, characteristic

of one with signi�cant quantities of intermittent renewables deployment, more

signi�cantly rewarded �exible operation.

Keywords: Flexible CCS, Dynamic optimisation, Dynamic process

modelling, multi-period design, gCCS

1. Introduction

Carbon capture and storage (CCS) has been proposed as a means to enable

a least-cost transition to a low carbon energy system and is also important

for industrial sectors [1], [2]. Given the increasing penetration of intermittent

renewable electricity generation and the in�exible nature of traditional nuclear5

power generation 1, decarbonised power plants need to be designed for �exible

operation in order to be able to promptly respond to variation in electricity

demand [3], [4], [5] and to exploit the associated variation of electricity prices,

while maintaining the carbon intensity of the plant at low levels[6], [7], [8], [9].

Flexible capture can be achieved in a range of ways. At the level of an individual10

power plant, �exible operation can be achieved using measures such as adding

a solvent storage tank, bypassing the capture facility for certain time periods or

operating the capture facility at di�erent capture rates according to electricity

output requirements.

To the best of our knowledge, the concept of �exible operation, was �rst15

introduced by Gibbins and Crane [10] in 2004, noting that this study makes

1It is recognised that small modular reactors (SMRs) have the potential to o�er a �exible

form of nuclear power but at the time of writing, this is a relatively immature technology, and

has not been widely deployed.

2

reference to private communication with Prof Rochelle 2 on this subject in 2002.

In the 2004 study, the concepts of solvent storage and exhaust gas venting (or

capture bypass) were �rst introduced. It this study, it was concluded that

solvent storage had the potential to reduce electricity costs by 6 -7% and that20

exhaust gas venting was a viable strategy in the event that electricity prices

($/MWh) were 2 -3 times greater than carbon costs ($/tCO2). Here, in the case

of solvent storage, an approximation of the additional capital cost associated

with the infrastructure required for solvent storage was provided, but a detailed

design of that equipment was not performed. Following this study, several25

contributions focused on �exible operation of the capture process as a way to

improve the economics of CCS power plants either by reducing the capture level

through exhaust gas venting, by storing the solvent using rich and lean amine

storage tanks or by varying the degree of solvent regeneration [5], [6], [7], [8],

[11],[12], [13], [14], [15], [16], [17] [18], [19], [20], [21], [22], [23], [24], [25], [26],30

[27], [28], [29], [30].

With the exhaust gas venting option, the power plant operates with partial

or no capture of the CO2. Under this strategy, the energy required for solvent

regeneration is anticipated to be reduced or eliminated by venting a portion of

the exhaust gas directly to atmosphere. Thus, the steam that would have been35

used for solvent regeneration is instead not extracted, resulting in increased net

power output. From a practical perspective however, it may not be the case

that all of the steam could be redirected to the LP turbine. It is important to

note that the duration of the periods for which exhaust gas would be vented

in response to a peak in electricity prices would likely be relatively short - on40

the order of 2 - 5 hours [31]. During this time, there are likely two options

for operating the capture plant: 1. continue to circulate the solvent through

the plant as normal and 2. stop the solvent circulation and allow the plant's

solvent inventory to accumulate in the sumps and pipework. Option 1. has

the advantage that it is ready to begin scrubbing CO2 from the exhaust gas45

2Prof G. T. Rochell, U. Texas at Austin.

3

as the plant is essentially "idling". However, as the solvent is circulated, it

will likely cool relatively rapidly as it moves from the well-insulated desorption

process to the absorption process which may be open to the atmosphere. This

would likely lead to a rapid cooling of the solvent towards ambient temperature

in addition to the potentially signi�cant losses of volatile organic compounds50

(VOCs) to the atmosphere. This may mean that there will be a non-negligible

delay in returning the capture plant to its normal set-point of capturing 90% of

the CO2 - thus potentially incurring a substantial cost associated with emitting

CO2 during periods of relatively low electricity prices. This may well undo

much of the pro�tability bene�t associated from venting the exhaust gas in55

the �rst place. Further to this point is the potential for increased emission

of VOCs, which could potentially compromise a facility's license to operate.

Option 2. has the advantage that it avoids much of the solvent cooling e�ect

and also the VOC emission. However, there will be a delay associated with

bringing the solvent circulation back to a steady state of operation such that60

the capture plant is again ready to capture CO2. Thus, this may also result in

the imposition of increased costs associated with emitting CO2 during periods

of reduced electricity prices. To the best of our knowledge, neither of these

points have been addressed in the literature to date, and represent clear and

important avenues for future research. In the solvent storage mode, the CO265

capture level is kept constant and solvent storage tanks (rich and lean) are

used to shift the regeneration load to times when the electricity price (and

thus the economic opportunity cost associated with solvent regeneration) is low.

Following the work of Gibbins and Crane, Rao and Rubin [11], identi�ed the

most cost-e�ective level of CO2 capture using the exhaust gas venting option.70

They concluded that the optimal CO2 capture level is dependent on plant size

and, if exhaust gas venting is considered, the cost-e�ectiveness of CO2 capture

can be improved. The importance of electricity and CO2 price variations in

determining the cost-optimal level of CO2 capture has since been shown by

several authors [6], [7], [14], [16], [18], [25], [32], [33]. In their work, Haines and75

Davidson [6], reviewed the ability of the main capture technologies (pre-, post-

4

and oxy- combustion) to modify their operation and design to provide some

economic peak power capability. To our knowledge, this contribution is unique

in that it evaluates the potential of these three types of CCS to operate �exibly.

A key conclusion of their analysis was that post-combustion systems o�ered the80

greatest possibility of operating �exibly. This makes intuitive sense, as given

that between pre-, post- and oxy-combustion capture, the nominal electricity

output penalty of post-combustion CO2 capture is typically considered to be the

greatest [1], [2], it therefore stands to make the largest relative gain by reducing

this penalty at opportune times.85

An important caveat is that the majority, if not all, of these studies were

performed using aqueous solutions of 30 wt% monoethanolamine (MEA) as a

solvent. This solvent typically requires 3.5 - 4.2 GJ/tCO2captured [2], and as

such imposes a large electricity output penalty on the power plant. However,

the current industrial state-of-the-art solvents include Shell's Cansolv, Fluor's90

Econamine or MHI's KS-1 solvents which typically use higher concentrations

of active ingredient (typically between 40 - 50%) and have an energy of re-

generation of 2.33 GJ/tCO2 [34], [35] 2.8 - 3.0 GJ/tCO2 and 2.5 -2.8 GJ/tCO2

respectively. Importantly, all of these solvents require a similar quality (temper-

ature) of steam for solvent regeneration, therefore a lower energy of regeneration95

leads to a reduced electricity output penalty. Moreover, solvents o�ering fur-

ther improvement are on the horizon, such as those reported by Ye et al. [36]

wherein materials requiring 2.0 GJ/tCO2at temperatures of 80 - 100◦C are re-

ported. We can readily evaluate the impact that these advanced solvents have

on process performance using the IECM tool [37]. IECM indicates that the100

higher heating value (HHV) e�ciency of an ultra supercritical (USC) power

plant is 42.83%. Its worth noting at this point that IECM is a relatively conser-

vative tool, and current USC plants in service today exhibit HHV e�ciencies of

44% and above. So-called advanced ultra supercritical (AUSC) plants have the

potential to operate with steam temperatures of above 700◦C and with HHV105

e�ciencies in the region of 47 - 48% [? ], [38]. Then, applying amine-based CO2

capture to the IECM USC power plant reduces the HHV e�ciency to 29.03%.

5

Using Fluor's FG+ solvent (as described above) results in an HHV e�ciency

of 33.25%, MHI's KS-1 solvent gives an HHV e�ciency of 33.73% and �nally

Shell's Cansolv solvent gives an e�ciency of 34.33%. Similar calculations using110

an oxy-combustion option gives an HHV e�ciency of 36.58% - still greater than

the post-combustion options, but the gap is reduced. At this point, it is worth

noting that the average annual HHV e�ciency of the existing US coal-fueled

electricity generating �eet is approximately 32%, and this can be substantially

lower in some parts of the world [38]. In other words, through the deploy-115

ment of state-of-the-art power and capture plant technology, it is conceivable

that decarbonised coal-�red power generation could be more e�cient than it is

today.

Con�rming the results presented by Rao and Rubin [11], stopping the sol-

vent regeneration during peak hours increases electricity generation by 20%.120

However, owing to the range of CO2 and electricity prices assumed in their

analysis, the additional revenue derived from selling the electricity was quite

small; between 0 - 4% of additional revenue above the baseline scenario. A key

limitation to the enhanced pro�tability that may be derived from �exible op-

eration is the compromise between peak electricity prices and CO2 prices - the125

peak electricity price needs to be signi�cant to o�set the additional cost associ-

ated with the emission of additional CO2. A potential limitation of this study

is that it performed its analysis based on the UK's electricity system in the �rst

decade of the 21st century. In this period, the electricity system was composed

of nuclear, coal- and gas-�red power stations, and - in line with their analysis -130

the electricity market would not be characterised by excessive peakiness. Going

forward, as the UK experiences increased deployment of intermittent renewable

power [31], an upwards pressure may be expected on electricity prices and the

electricity market may be characterised by an increased peakiness.

This link between peak electricity prices and costs associated with CO2 emis-135

sion was also observed in the work of Ziaii et al.[14], who reported that �exibility

may improve the annual operating pro�ts; however, the balance of the electricity

and CO2 price needs to examined. In their work, Chalmers et al.[15], [16], pre-

6

sented an updated version of Gibbins and Crane's 2004 analysis and discussed

the �exible operation of coal �red power plants with post-combustion capture.140

They identi�ed exhaust gas venting and solvent storage as two options. The

conclusion of this work was that exhaust gas venting is economically valuable

if the price per MWh was two to three times higher than the cost per tonne

of CO2 emitted, and that solvent storage signi�cantly reduces the CO2 price

at which exhaust gas venting is economically attractive, repeating the earlier145

conclusions of Gibbins and Crane [10]. Whilst both Chalmers et al [15], [16]

and Gibbins and Crane [10] noted that solvent storage would come at an addi-

tional cost, a detailed design of the required solvent storage infrastructure was

not performed in either of their analyses. In their work, Cohen et al. [7], have

created optimisation and rule-based models within the General Algebraic Mod-150

eling System (GAMS) [39], to study pro�t-maximising operation of a coal �red

power plant with �exible CO2 capture with and without solvent storage under

varying degrees of electricity price foreknowledge. They concluded that the gas

venting option is unpro�table at high CO2 prices (above $70/tCO2), while sol-

vent storage maintains a 9-29% pro�t at any CO2 price, highlighting the value of155

�exible CCS. In their work, Chalmers et al. [18], performed a �rst order techno-

economic screening analysis to determine whether solvent storage could be an

important factor to contribute to the economic performance of the power plant.

They concluded, similarly to Haines et al. [6], that �the revenue increase which

could be obtained in any one day by using solvent storage varies considerably160

depending greatly on the shape of the daily electricity price curve� and �could be

an attractive option in some electricity networks�. When discussing �exible op-

eration, it is important to bear in mind additional capital equipment costs, i.e.,

storage tanks, oversized power plant equipment, such as larger reboilers and so

forth [16], [6], [21] [40]. In their work, Patiño-Echeverri et al. [32], presented an165

analysis on the di�erent electricity prices for an amine-storage to a CCS system.

They examined two di�erent plants (existing subcritical and new supercritical)

and two design modes of the storage tank; two-mode and three-mode. In a two-

mode system the solvent regeneration system has a binary mode of operation

7

and either runs at 100% or 0% capacity. In the three-mode system, the solvent170

regeneration process might (1) run at 100% capacity to regenerate both the sol-

vent �owing from the absorber and the stored solvent from the storage tank, (2)

run at 0% capacity, or (3) operate so as to regenerate only the volume of solvent

required for the absorber at that time. The study of Patiño-Echeverri et al. [32]

stands out as one which does perform a detailed engineering design of the solvent175

storage tanks. Here, they assume that the additional volume of solvent will cost

between $629-711/m3 and the total cost for the additional solvent and storage

tanks will be $6.8M and $2.5M respectively. Here, carbon steel storage tanks

were assumed, and, as noted by Haines and Davison [6], solvent degradation

e�ects would need to be properly taken into account. It may be that stainless180

steel storage tanks would we required, which could substantially increase the

associated capital cost. They found that the required price di�erential was in

fact a complex function of the cycling period, the capacity factor, the storage

size, and whether the plant is a retro�t or new. The required price di�erential

for two-mode operation ranged from $40-111/MWh for daily cycling and $92-185

677/MWh for weekly cycling. In the three-mode case the range was found to

be $43-$141/MWh for daily cycling, and from $110/MWh to $285/MWh for

weekly cycling. In general new plants require much higher price di�erentials to

justify investment in solvent storage. This makes intuitive sense as the up-front

capital expenditure of post-combustion CCS is already signi�cant and current190

research e�orts are prioritising its reduction as a means to reduce the $/MWh

cost of CCS electricity.

In their work, Versteeg et al. [25], considered the pro�tability of coal and

natural gas-�red power plants with amine and ammonia post-combustion CO2

capture for variable electricity prices. They have concluded that the solvent stor-195

age option increased pro�tability at low carbon prices (¿40-60/tCO2). Husebe

et al. [21], developed an mixed integer linear programming (MILP) model to

identify the optimum operating strategy of a coal �red power plant with post-

combustion capture, and evaluated the potential value of �exible solvent regen-

eration and storage. The results showed that �exibility can lead to increased200

8

pro�ts, particularly in volatile electricity markets. Finally, a correlation be-

tween pro�tability and cyclical (weekly, seasonally, etc.) demand patterns was

observed. However, �exible operation is limited by case speci�c paramters, such

as the size of the storage tanks or the maximum size of the desorber, which

needs to be taken into account in the techno-economic analyses. In their work,205

Brasington et al. [40], presented an integrated coal �red power plant with a

post-combustion CCS plant. They concluded that the operational complexity

increases with solvent storage and due to increased operational and capital costs

imposed, the pro�tability of the plant does not increase for long periods of stor-

age (hours). They added that there might be a potential for short duration of210

solvent storage (i.e., less than 30 min), since this will not increase the opera-

tional complexity of the conventional coal �red power plant. The most recent

contributions on the �exible operation of CO2 capture systems are reported by

Van Der Wijk et al. [27], Oates et al.[5], Mac Dowell and Shah [8], Zaman

et al.[28], and Adams and Mac Dowell [41]. In their work, Van Der Wijk et215

al. [27] showed that the �exible options are not utilised. This is due to ei-

ther the prevailing CO2 prices in Europe which do not favour the exhaust gas

venting options or the regeneration constraints of the base load power plant

for the solvent storage. However �exible CCS plants can increase the reserve

capacity provision by 20-300% compared to non �exible plants. The paper of220

Oates et al.[5] is the �rst to discuss the �exibility options available to a natural

gas �red power plant with post-combustion capture using MEA as a solvent.

Their framework incorporated both a design and operating optimisation model

to explore the exhaust gas venting and solvent storage as �exible options. The

concluded that �exible CCS could result in an increased pro�t in the range 0-225

35% depending on the design of the regenerator and capacity of the solvent

storage tanks. In the majority of the previous studies, the decision variables

for �exible operation were primarily the capture level for exhaust gas venting

scenarios and the regeneration rate for solvent storage options. These variables

were not treated as optimisation variables but were varied according to di�erent230

energy prices. Adams and Mac Dowell presented a detailed study of a CCGT

9

integrated with a CO2 capture and compression process. Here, the performance

of this system was evaluated under part-load conditions, with a key observa-

tion being that the whilst the cost structure of the integrated process remains

approximately constant for o�-design point operation, this will appreciably in-235

crease the levelised cost of electricity (LCOE) of these plants, which may have

implications for the average price of electricity of the systems in which these

processes are integrated. Two recent contributions discuss the rigorous optimi-

sation of �exible CCS systems; that of Mac Dowell and Shah [8] and Zaman

and Lee [28]. In their work, Zaman [28], presented an optimisation model for240

a post-combustion capture model for three �exible con�gurations: exhaust gas

venting, solvent storage and combination. Compared to the base case, the three

modes of operation showed 3.04%, 10.1% and 11.08% savings, respectively.

In our previous work [8], we presented a multi-period optimisation problem

to evaluate the pro�tability of a load following coal �red power plant integrated245

with a post-combustion capture plant for three di�erent operating strategies.

As an addition to the literature on this subject, this paper introduced the con-

cept of variable solvent regeneration (VSR) as another strategy of the �exible

operation of the capture plant. In this study, it was shown that allowing CO2

to accumulate in the working solvent during periods of high electricity prices250

and the subsequent regeneration of the solvent during periods of low electricity

prices o�ered substantially improved pro�tability over either venting exhaust

gas or solvent storage. In the case of solvent storage, a key limiting factor was

found to be the quantity of steam available from the power plant, and in the

case of exhaust gas venting it was found that, in order to capture 90% of the255

CO2 produced by the power plant, it simply was not possible to vent a substan-

tial portion of the CO2, and similarly to previous work, the venting of CO2 was

observed to incur a substantial cost.

In this contribution we present a comprehensive study of the options for

maximising pro�ts via �exible operation whilst maintaining a low average car-260

bon intensity (kgCO2/MWh) for both coal- and gas-�red power stations. A

multi-period, dynamic optimisation problem is formulated and implemented in

10

the gCCS toolkit3 and solved using the default solvers available within gPROMS

4. Using a load-following plant as the base case scenario, we consider three op-

tions for �exible operation of both coal- and gas-�red power plants: exhaust265

gas venting, solvent storage and time-varying solvent regeneration. In the case

of the solvent storage option, we calculate the capital cost associated with the

storage tanks and then mediate that as an increase in the short run operating

cost of the plant.

The remainder of this paper is laid out as follows: In section 2, we present our270

approach for calculating electricity prices over the course of a 24 hour period, the

power plant and capture plant models and the optimisation problem. Section 3

presents the results and discussions for the di�erent scenarios and in section 4

we present the conclusions of our work.

2. Model development275

2.1. Consideration of the electricity system in which CCS will operate

Many techno-economic analyses of CCS in the literature assume steady state

operations of the plant, consistent with baseload power generation [5], [42], [43],

[44], [45]. However, as noted in the introduction, it is quite unlikely that CCS

power plants will operate in a baseload fashion in many electricity markets.

Rather, they may be required to operate in an electricity system containing

a large proportion of intermittent renewable energy and will consequently be

required to provide a �exible, load-following service. A model price pro�le for a

twenty four hour period was constructed by calculating the short run marginal

cost (SRMC) for di�erent types of plants (super-critical pulverised coal (SCPC),

combined cycle gas turbines (CCGT) and open cycle turbines (OCGT) using

3 Process Systems Enterprise. (2014). gCCS overview. Retrieved September, 9, 2014,

from: http://www.psenterprise.com/power/ccs/gccs.html.4Process Systems Enterprise, gPROMS, www.psenterprise.com/gproms, 1997-2015.

11

the following equation:

£SRMC

MWhr=

£MWhrFuel

nplant+ (£CO2

Tonne · CITonnesCO2

MWhr ) +£V arO&M +£CO2

T&S (1)

where ¿SRMC is the SRMC of the electricity generated by a given plant. In this

calculation the variable operating and maintenance costs (¿V arO&M ) and �xed cost

(¿CO2

T&S) for transport and storage are also considered. The data for this equation

were obtained from the Department of Energy and Climate Change (DECC) [46]280

for the fossil fuel prices and carbon prices and by the Joint Research Centre of

the European Commission [47] for the e�ciencies and carbon intensities. These

values are presented in Table 1.

12

Table 1: Values from DECC and the JRC for use in evaluating Equation 1. The values reported here are for the central scenario by DECC. However,

in this work a sensitivity analysis has been performed for fuel, carbon and electricity prices (more details in section 3.1.5 and 3.2.5) The e�ciency

values reported here are for plants with no CCS. In Equation 1, we impose an 8-10% penalty on the power plant. The carbon intensity is for a

capture plant operating at 90% capture for SCPC and CCGT while the OCGT operates in an unabated fashion.

Fuel price

(¿/MWh)

nplant CO2 price

(¿/tonneCO2)

CI

(tonneCO2/MWh)

VarO&M

(¿/tonneCO2)

T&S

(¿/tonneCO2)

Electricity

price

(£/MWh)Central scenario SCPC 9.86 55 70 0.07 4.38 19.60 42.40

CCGT 24.53 60 70 0.04 3.06 19.60 62.62

OGCT 24.53 42 70 0.49 1.53 19.60 99.94

High scenario SCPC 13.89 55 105 0.07 4.72 32.20 61.30

CCGT 35.04 60 105 0.04 3.86 32.20 90.40

OGCT 35.04 42 105 0.49 1.93 32.20 144.95

Low scenario SCPC 7.17 55 35 0.07 4.02 8.20 27.15

CCGT 14.02 60 35 0.04 2.46 8.20 35.40

OGCT 14.02 42 35 0.49 1.23 8.20 55.04

13

Following our previous work [8], it is then assumed that over-night (o�-

peak) electricity prices will be set by SCPC plants, day-time prices will be set285

by CCGT plants with morning and evening peaks serviced by OCGT plants

[8]. This is illustrated in Figure 1, where the variable electricity pro�le for a

24h period is presented. As can be observed, there are 6 distinct periods of

operation: 2 peak periods (06:00-10:00 and 16:00-19:00) and 4 o�-peak periods.

In Figure 1, the capacity factor of the load-following plant is also illustrated.290

Figure 1: Illustration of the multi-period optimisation concept. In this graph the red line

represents the electricity price for the base case. The black line represents the scenario with

negative electricity prices. The dashed lines are the price di�erential (PD) between high and

low electricity prices for the two cases. From this it can be observed that there are 6 distinct

periods of operation denoted by the change of electricity prices within the 24h period. The

blue line represents the power plant capacity factor illustrating the load following pro�le of

the power plant. The question we are addressing here is what is the optimal operation of the

capture plant in order to maximise the pro�t during these periods.

2.2. Pulverised coal-�red power plant

A model of a supercritical pulverised coal power plant (SCPC) was developed

using the SCPC model provided by the gCCS toolkit [48], illustrated in Figure

2. The inputs of the model are the nominal power output, inlet and outlet steam

14

conditions of the LP turbine, and �owrate of steam extracted as a function of295

the CO2 captured. Steam is extracted at the inlet of the LP turbine.

gCCS 1.1.0

1 Model descrip on

The PCPP_high_level model is used to simulate the retrofit of a Pulverised Coal Power Plant and its integra on witha capture plant. This model is not suitable for part-load calcula ons. The main inputs of the model are the nominalpower output, inlet and outlet steam condi ons of the LP turbine and flowrate of steam extracted. Steam is extractedat the inlet of the LP turbine. This opera on can be carried out while keeping the coal flowrate or the power outputof the plant. If the former op on is chosen, the model calculates the power penalty of the LP turbine. This penalty isconsidered the same as the total power plant penalty. If the second op on is selected, the model calculates the coalflowrate required to produce the steam needed to keep the power output andmeet the steam demand of the captureplant.

1.1 Ports

The PCPP_high_level model has three inlet and three outlet material ports. All ports are shown on the model icon infigure 1 and described briefly in table 1.

1

2

3 4

5

6

7

Figure 1: Ports of the PCPP_high_level model.

2 Model specifica ons

The default view of the specifica on dialog for the PCPP_high_level model is shown in figure ??.

Commercial in confidence©Process Systems Enterprise Ltd (2016)

PCPP_high_level – Page 2

Figure 2: This model is used to simulate a supercritical pulverised coal power plant and its

integration with a capture plant. The SCPC high level model has three inlet and three outlet

material ports.1-Coal inlet, 2-Air inlet, 3-Waste outlet, 4-Flue gas outlet, 5-Condensate inlet,

6-Steam outlet. Port 7 is the cost port for connecting to the cost model. The inputs are the

nominal power output, inlet and outlet steam conditions of the LP turbine and �owrate of

the steam extracted.

The electricity output of the standalone power plant model is 500 MWe,

while integrated with capture is 440 MWe. Compression is not considered in this

work, since beyond recycling CO2, it cannot provide �exibility. The e�ciency

of the standalone SCPC model is 44%, while integrated with the capture plant300

is 38 %. These values agree with the literature where 6% e�ciency penalty

is reported for post-combustion capture with MEA [49]. The nominal power

output along with the temperature and pressure of the steam at the extraction

point is speci�ed (T=506 K, P=3.9 bar), while the �owrate of the required

steam is calculated by the capture plant by setting a value at the capture rate,305

taken to be 90% as a base case in this work.

15

2.3. Combined cycle gas-�red power plant

A model of a Combined Cycle Gas Turbine plant was developed in order

to specify the �owrate and composition of the �ue gas stream supplied to the

capture plant as well as the �owrate and thermodynamic state of the steam310

provided for the regeneration of the solvent. This model is based on the CCGT

model provided by the gCCS toolkit [48] as it is illustrated in Figure 3.

gCCS 1.0

CCGT high level

CCGT Power Plant(high level)

Contents1 Model descrip on 2

1.1 Ports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Model specifica ons 2

This document is the property of Process Systems Enterprise Ltd.

No part of the material contained herein may be copied, distributed, retransmi ed ormodified, in any way without the prior wri en consent of Process Systems Enterprise Ltd.

Commercial in confidence©Process Systems Enterprise Ltd (2015)

CCGT high level – Page 1

1

2

3

4

5

Figure 3: This model is used to simulate a combined cycle gas-�red power plant and its

integration with a capture plant.The CCGT high level model has three inlet and two outlet

ports. 1-Fuel inlet, 2-Air inlet, 3-Flue gas outlet, 4-Steam outlet, 5-Condensate inlet. The

inputs are the number of steam and gas turbines, the combined cycle e�ciency (library or

user-speci�ed) and the temperature an pressure of the stem extracted.

For the CCGT model, we have used the Siemens SGT5-4000F with the usual

con�guration of one gas turbine and three steam turbines. The electricity output

of the standalone power plant model is 421 MWe, while integrated with capture315

is 395 MWe; 73% of the total power comes from the gas turbine while the

remainder is provided by the steam turbines. The e�ciency of the standalone

CCGT model is 59%, based on the Siemens SGT5-4000F gas turbine [50], while

16

integrated with the capture plant is 53%. This is a 6% e�ciency penalty for

capture which agrees with the literature [49], [41].320

The gas turbine performance is changed by anything that a�ects the density

and/or mass �ow of the air intake to the compressor. As the ambient temper-

ature increases, the air density reduces and so the mass �ow to the compressor

is reduced, reducing the system output and e�ciency. The temperature e�ect

is dependent on the turbine model, as it depends on the cycle parameters and325

component e�ciencies. In order to account for the performance variation as a

function of the ambient temperature we specify the gas turbine e�ciency change

(-0.1%/◦C) and the exhaust �ow change (-0.45 kg/s/◦C) per degree ambient

temperature change, following previous studies [51], [52]. This is very impor-

tant, since in cases when the air temperature is very high, a pre-cooling system330

may need to be included. For example, for Australia, India or the GCC region

where air temperatures and humidities (and thus densities) are substantially

di�erent to those of the UK and can vary signi�cantly over the year. At the

extraction point, the temperature and pressure of the steam should be speci�ed

(T=500K, P=3.5bar). Similarly to the coal plant, this allows the calculation335

of the electricity output penalty associated with the operation of the capture

plant. The model also performs a check to determine whether the steam �ow

requested by the capture plant is available and �ags a warning if the �ow is

likely to be grater than 90 % of the total �ow in the turbine, thus protecting

the components of the CCGT from technical failure. Finally, the natural gas340

composition used in this work is: 95.2% CH4, 3.26% C2H6, 1.03% C3H8 and

0.51% C4H10 [53].

2.4. Capture plant model

The model of post-combustion CO2 process has been modelled using the

gCCS toolokit as illustrated in Figure 4.345

17

gCCS 1.0

Amine_capture_high_levelgCCS Model Documenta on

CO Capture2

(high level)

Contents1 Model descrip on 2

1.1 Inlets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Outlets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Model Specifica ons 2

This document is the property of Process Systems Enterprise Ltd.

No part of the material contained herein may be copied, distributed, retransmi ed or modified, in any waywithout the prior consent of Process Systems Enterprise Ltd.

Commercial in confidence©Process Systems Enterprise Ltd (2015)

Amine_capture_high_level – Page 1

CO2 to stack CO2 to compression

Flue gas inletSteam inlet

Condensate outlet

Figure 4: This model is used to simulate a post-combustion CO2 capture plant. It has two

inlets, the �ue gas inlet and the steam inlet and three outlets; the treated �ue gas directed to

the stack, the CO2 to compression and the condensate outlet from the reboiler.

The speci�cations for this model are the gas outlet temperatures for the heat

balance and the CO2 capture rate which is set at 90%. From a list of di�erent

solvents (MEA (monoethanolamine), DEA (diethanolamine), DGA (Diglyco-

lamine), MDEA (methyldiethanolamine) we have chosen a 30% wt MEA solvent

with 0.23 lean loading and 0.5 rich loading. These speci�cations are in turn used350

to determine the required solvent �owrate.

An illustration of the integrated coal- and gas-�red power and CO2 capture

plants, are illustrated in Figures 5 and 6, respectively.

18

Figure 5: Integrated SCPC power plant and post combustion plant. There are three connec-

tion points between the power and capture plant. The exhaust gas �ow rate from the power

plant, going in the absorber column of the capture plant, the steam inlet to the reboiler of

the capture plant for the solvent regeneration from the LP turbine and the condensate return

to the power plant. A stack is also used for the treated �ue gas.

19

Figure 6: Integrated CCGT power plant and post combustion plant. There are three connec-

tion points between the power and capture plant. The exhaust gas �ow rate from the power

plant, going in the absorber column of the capture plant, the steam inlet to the reboiler of the

capture plant for the solvent regeneration from the LP turbine and the condensate return to

the power plant. A stack is also used for the treated �ue gas. In this model we can also see

an additional stack which is used for the exhaust gas venting scenario, where untreated �ue

gas is emitted to the atmosphere.

2.5. Optimisation problem

In this study we have distinguished between the Degree of Capture (DoC)

and the Integrated Degree of Capture (IDoC) as described in the following

equations:

DoC = 100 · (COGenerated2 − COEmitted2

COGenerated2

) (2)

IDoC =

∫ tf

t0

DoC dt (3)

where t0 and tf are the start and end periods of interest.355

The dynamic optimisation problem solved in this study is based upon the

theory of Grossman and Sargent, originally intended for the optimum design

of multi-purpose chemical plants [54]. The design and multi-period operation

20

of the decarbonised power plant can be represented by the system of mixed

di�erential and algebraic equations of the form:

f(x(t), y(t), u(t), v, x(t)) =0 ∀t ∈ [0, tf ] (4)

where x(t) and y(t) are the di�erential and algebraic variables in the model,

while x(t) are the time derivatives of the x(t). The control variables, u(t), and

the time invariant variables, v, are to be determined by the optimisation. In

this study, the control variables u(t) include the bypass fraction to storage for

the solvent storage scenario, the bypass fraction and the lean solvent �owrate360

for the exhaust bypass scenario and the lean solvent loading for the time varying

solvent regeneration scenario.

In some applications it is necessary to impose certain conditions that the

system must satisfy at the end of the operation, i.e the end-point constraints.

These can be equality or inequality end point constraints of type:

w(tf ) = w∗, wmin ≤ w(tf ) ≤ wmax (5)

where w is one of the sytem variables (x or y).

In our case, the end-point constraints were that the IDoC would be in the

range 89.9-90%, that there is no CO2 accumulation at the end of the optimisa-365

tion period for the solvent storage scenario and that the lean loading is at the

same value at the end of the optimisation period as that at the beginning for

the variable solvent regeneration scenario.

Our problem is also subject to path constraints in the case of the solvent

storage and exhaust gas venting scenarios:370

wmin ≤ w(t) ≤ wmax ∀t ∈ [0, tf ] (6)

In the solvent storage scenario, the bypass fraction to storage should be

between -1 and 1, the exhaust gas by pass fraction between 0 and 1, the lean

solvent �owrate between 0 and 1000 kg/sec (704 kg/sec are required for 90%

capture at full plant capacity) and the DoC to be in the range of 0-100%.

21

The constraints and decision variables for each of the four scenarios are375

presented in Table 2.

The dynamic optimisation seeks to determine the time variation of the con-

trol variables u(t) over the time horizon t ∈ [0, tf ] so as to maximise the �nal

value of a single variable z subject to constraints (5)-(7):

maxu(t),t∈[0,tf ]z(tf ) (7)

where z(tf ) is the short-run marginal cost (SRMC) pro�t of the plant.

Table 2: Constraints and decision variables for the di�erent scenarios. The �rst column are

the scenarios for both the coal and gas-�red power plants with capture, the second column are

the path constraints which must be satis�ed at all times during operation, the third column

are the end-point constraints which must be satis�ed at the end of operation and the last

column are the decision variables of the optimisation problem. Fsolv is the solvent �owrate

in kg/sec, CO2acc is the CO2 accumulated in kg/sec, Frbp is the by pass fraction, Frst is the

fraction to storage, and α, β, γ are the parameters used in the equation of the lean loading

as a function of t for the solvent regeneration scenario

Scenario Path constraints End-point constraints Decision

variables

Load following DoC=90% - -

Solvent storage 0 ≤ DoC ≤ 100 89.9 ≤ IDoC ≤ 90,

CO2acc=0

Frst

Exhaust gas venting 0 ≤ DoC ≤ 100

-1 ≤ Frbp ≤ 1

0 ≤ Fsolv ≤ 1000

89.9 ≤ IDoC ≤ 90 Frbp, Fsolv

Time varying solvent re-

generation

0 ≤ DoC ≤ 100 89.9 ≤ IDoC ≤ 90 α, β, γ

3. Results and Discussion

In this section we present the results of our study. We start with the SCPC,

considering the load following, the solvent storage, the exhaust gas venting and380

the variable solvent regeneration scenarios and compare them by performing a

sensitivity analysis on carbon and electricity price di�erentials. We then present

the results for the same scenarios for the CCGT model.

22

For the solvent storage scenario to be pro�table, the bene�ts from the elec-

tricity price arbitrage need to exceed the signi�cant capital costs associated with385

building the solvent storage infrastructure [32]. In order to account for the cap-

ital cost in�uence on the storage scenario, we calculate the capital cost of the

storage tank as an additional marginal cost and we include it in the pro�t func-

tion. This is discussed in detail in section 3.1.2. We then perform a sensitivity

analysis on the electricity price di�erentials in order to explore the di�erence in390

low and high electricity prices at which this scenario is pro�table.

3.1. SCPC model

3.1.1. Load following

In the �rst scenario, the capture plant operates in accordance with the power

plant. As illustrated in Figure 1 the power plant ramps up and down during the395

period of one day with variable electricity prices, while the DoC and the lean

loading are kept at 90% and 0.23, respectively. In Figure 7, a sensitivity analysis

on the CO2 price is presented. A very low price of CO2 of ¿10/tCO2, can lead

to 7% increase in the total daily pro�t, as compared to the base case (black

line) in the �gure. For a capture rate kept at 90% and a very high carbon tax400

(¿200/tCO2) the decrease in the pro�t from the base case is 18%. This shows

that the carbon price is important to the overall pro�tability of this plant. A

further implication of this observation is that CO2 prices should be su�ciently

high to incentivise a high DoC, but no higher. Beyond a certain point, it simply

becomes punitive.405

23

Figure 7: Cumulative pro�t-SCPC-load following scenario. In this �gure we illustrate the

variation of CO2 prices as a function of the cumulative pro�t. The results of this sensitivity

analysis show that the carbon price has a signi�cant in�uence on the total pro�t and can lead

to 7% increase for low CO2 price to 18% pro�t decrease if the CO2 prices are more than 150%

of the base case (¿70/tCO2)

3.1.2. Solvent storage

In the solvent storage scenario, two solvent storage tanks are added between

the absorber and the stripper. If solvent storage is available then a portion of

the rich solvent can temporarily stored, rather than being sent to the desorber

for immediate regeneration. This stored rich solvent can then be subsequently410

regenerated by adding it to rich solvent generated by ongoing operations during

a period of relatively low electricity prices. Previously-stored lean solvent from

another tank is used to allow capture to continue. This scenario is illustrated

in Figure 8.

24

Flue gas

Rich Pump

Heat exchanger

Reboiler

Condenser

CO2 to compression

AbsorberDesorber

Lean Solvent

Cleaned flue gas

Steam

RICH SOLVENT STORAGE

LEAN SOLVENT STORAGE

Figure 8: Solvent storage �owsheet. This �gure presents the option of solvent storage in

order to increase power output during peak times. The rich solvent �ow is diverted from the

absorber to a rich solvent storage tank instead of routing to the stripper. The lean solvent

that is regenerated during low electricity price periods is stored to the lean solvent storage

tank used for capture during peak electricity price periods

The question in this scenario is how much solvent is sent to the storage tanks

during the period of the simulation, subject to the constraint that at the end of

the simulation the CO2 accumulated should be zero. The additional equations

which describe this optimisation scenario are:

Fsolvreb = Fsolv · (1− SF ) (8)∫ tf

t0

CO2acc = Fsolvsto · θRichsto · wsolvent ·min(0, SF )dt (9)

where, Fsolvreb is the solvent storage directed to the reboiler, Fsolv is the to-415

tal solvent �owrate for 90% capture, Fsolvsto is the amount of solvent stored,

wsolvent is the mass fraction of the solvent, θRichsto is the rich loading of the solvent

stored and SF is the split fraction. If SF is less than zero, then the rich solvent

is being regenerated whereas when SF is greater than zero, the rich solvent is

being stored.420

The results of this optimisation problem are presented in Figure 9. As

25

can be observed, there are two periods of solvent storage during periods of

high electricity prices (06:00-10:00 and 16:00-19:00) and four periods of solvent

regeneration, with higher regeneration at the low electricity prices (¿55/MWh)

and lower regeneration at electricity prices of ¿70/MWh.425

Figure 9: Storage volume of rich and lean tanks for a storage volume of 10,000 m3. The blue

line shows the electricity price variation within the day. The black dashed line is the solvent

stored in the lean tank and the solid black line is the solvent stored in the rich tank. During

high electricity prices the rich tank is �lling up and the opposite during low electricity prices

Additional capital expenditure is required for the storage tanks which depend

on a number of factors, such as the volume of solvent required per kg of CO2

absorbed and the mass of CO2 absorbed. For a 30% MEA loading and a lean

loading of 0.25 the required capacity for storage is 10,000 m3 for 90% capture in

order to have a net power output increase of around 20% [16], and this is what430

we have used as a base case for this study. However, since the size of the storage

tanks a�ects the pro�t, we have performed a sensitivity analysis to show this

variation as presented in Figure 10. At the start of the optimisation, the initial

level at the rich tank is 0.3 m and at the lean tank is 0.7 m.

When considering the solvent storage, the capital costs of the storage tanks435

26

need also to be considered. In this work we assumed that the storage tanks used

are erected on site and are composed of stainless steel 304 [5]. We estimated the

capital costs of the tanks using Couper's Handbook [55] as described in equation

(10).

CCst = 0.72 · 1.218 · FM · exp[11.662− 0.6104 · (lnV ) + 0.04536 · (lnV )2] (10)

where CCst is the capital cost for the storage tank in ¿and FM is 3.4 for a440

stainless steel tank 304. As Couper provides costs in 2003 US$, we then escalated

these costs to 2015 using the Chemical Engineering plant cost index (CEPCI)

[56], as described in equation (11), and converted them to ¿GB, assuming a

currency conversion of $1.5/¿.

CCst2015 = CCst2003 ·CEPCI2015CEPCI2003

(11)

where CEPCI2015 was 550.4 and CEPCI2003 was 402.445

Given that the addition of the solvent storage infrastructure is analogous to

an e�ciency improvement, it is reasonable to require a short payback period

for this investment. As a base case in this analysis, we have selected a three

year payback period. For the 10,000 m3 tank, using equations (10) and (11)

we calculate the capital cost to be ¿715k. We then divide this cost by 1095 in450

order to calculate the cost per day and then divide this by the integrated net

power output of the power plant with solvent storage to transform the capital

cost into ¿/MWh. The SRMC of the plant is reformulated to include this cost:

£SRMC

MWhr=

£MWhrFuel

nplant+ (£CO2

Tonne · CITonnesCO2

MWhr ) +£V arO&M +£CO2

T&S +£SS

(12)

where ¿SS is the cost of the storage tank.

We have also considered the case where the capital cost has been paid o�455

(after 3 years) by solving the aforementioned optimisation problem without

considering the cost of the storage tanks.

27

In Figure 10, we can observe the results on the total pro�t of the solvent

storage scenario compared to the base case load following scenario and evaluate

it for di�erent solvent storage capacities, in each case accounting for the capital460

cost of the storage tanks. As can be observed, the cumulative pro�t of the base

case scenario (black column) is always greater than the solvent storage scenario

for any size of the solvent storage tanks (grey columns) when considering the

capital cost of the tanks with payback time 3 years. Since the capital cost of the

storage tanks decreases as the storage tank size decreases, the di�erence between465

the cumulative pro�t of the base case and the solvent storage cases decreases

from 2.2% for a tank of 10,000 m3 to 0.4% for a very small storage tank of 500

m3. This shows that, for the scenarios considered here, the additional electricity

sold cannot outweigh the capital cost associated with the storage tanks. When

the payback period is increased to 10 years (blue columns), then the solvent470

storage scenario is more pro�table than the base case and this pro�t increases

as the solvent storage size increases.

28

Figure 10: Pro�t margin between the load following base case scenario and the solvent storage

scenario with and without the capital cost of the storage tanks for di�erent storage tank

sizes. This �gure shows a comparison between the base case scenario (black column) and the

solvent storage scenario with capital cost with 3 years payback period (grey columns), 10 years

payback period (blue columns) and without capital cost (red columns) for di�erent solvent

storage sizes. For the case where solvent storage cost is considered with 3 years payback period,

as the capital cost of the storage tanks decreases with the size, the cumulative pro�t increases.

The revenue from the electricity sold cannot outweigh the capital cost of the storage tank.

When the payback period increases to 10 years then the cumulative pro�t increases with the

storage tank size. After the capital cost has been paid o� (red columns) there is an increase

to the cumulative pro�t proportionally to the size of the tanks.

As was mentioned in the introduction, many studies in the literature exclude

the capital cost associated with the storage tanks, and conclude that the solvent

storage scenario can have an additional pro�t of approximately 3.5% [28]. We475

have also examined this option and arrive at the same conclusion - we achieve

an increase in pro�tability of 3.3% savings for the 10,000 m3 tank size relative

to the base case, after the solvent storage capital cost has been paid o�. This

pro�tability decreases as the size of the storage tanks decreases [28], which shows

that even if the capital cost for the large storage tank gives less pro�t during480

the payback period, after this time a larger storage tank gives more pro�t and

29

should be the one installed.

3.1.3. Exhaust gas venting

In the exhaust gas venting scenario, as illustrated in Figure 11 there is no

modi�cation to the plant's original design. In this scenario, a portion of the485

exhaust gases are re-directed upstream of the absorber to the stack and vented

directly to the atmosphere.

Figure 11: Exhaust gas venting �owsheet. The �ue gas from the power plant are re-directed to

the stack (black dashed line), mixed with the treated �ue gases and vented. No modi�cation

and extra investment is needed for this option

In this scenario, the power plant ramps up and down as illustrated in Figure

1 and in order to decouple the operation of the power and capture plants, we

consider the option of venting part of the exhaust gas during periods of high490

electricity prices.

Once again, the problem is solved subject to the end-point constraint of

30

89.9 ≤ IDoC ≤ 90. In this case, the vent fraction is 21% for both periods of

high electricity prices as illustrated in Figure12. The reason for this is that the

end point constraint of IDoC set to 90% requires the model to select venting in495

order to avoid solvent regeneration costs during periods of high electricity prices.

During the time periods with lower electricity prices the DoC is ∼ 100% to

make-up for what was emitted. However, we also solved this problem where the

end-point constraint was relaxed such that 89.9 ≤ IDoC ≤ 100, or in other words

the model was free to capture as much CO2 as was rational to maximise the500

pro�t. In this instance, for a CO2 price of ¿70/tCO2 , venting was not selected,

and the model solved for an IDoC = 100%. It was only when the CO2 price was

reduced to ¿10/tCO2that venting was selected. This is a potentially interesting

result. First, we must recall that in this problem, we are solving based on

maximising the pro�t on a short run marginal cost basis. Whilst this is how505

power plants are typically dispatched within an electricity market, the SRMC

does not include the capital or pre-development costs of the power and capture

plant. Therefore, care must be taken not to interpret this result as a inferring

that a CO2 price of ¿10/tCO2 is su�cient to incentivise the construction of the

CCS power plant given the electricity prices discussed in this paper. However,510

the CCS plant would have an economic lifetime of 40 years but would typically

aim for a payback period of 10 - 20 years, depending on rates of return required

by investors. Therefore there is likely to be a signi�cant period for which the

power plant is operating on a SRMC basis.

31

Figure 12: Exhaust gas venting scenario for the SCPC for a constrained IDoC. The black

line represents the fraction of the exhaust gas that is vented and the blue line presents the

electricity price variation. 21% of the exhaust gas is vented during periods with high electricity

prices while a DoC ∼ 100 % is chosen at all other times, leading to an IDoC of 90% at the

end of the day

3.1.4. Time varying solvent regeneration515

In this scenario, we use the working solvent as means to provide �exibility to

the power plant. This is achieved by allowing CO2 to accumulate in the solvent

during hours of peak electricity prices and regenerating the solvent during o�-

peak periods. The lean loading is therefore no longer a �xed variable as in the

previous scenarios but can vary with time as expressed in the following equation:

θtLean = αt · t2 + βt · t+ γt (13)

where the lean loading can vary in di�erent time periods t, based on the quadratic

expression above by varying the parameters α, β and γ. The only constraints

imposed are the IDoC should be 90% at the end of the optimisation and that

the lean loading is bounded between 0.15 and 0.5 over the course of the opti-

misation. The variable time is set to zero at the beginning of each new period.520

The results of this optimisation problem are presented in Figure 13.

32

Figure 13: Solvent regeneration scenario as a function of electricity price and degree of capture

(DoC) for the SCPC. As it is observed, instead of operating at a constant lean loading, the

lean loading varies within the day in sympathy with the variable electricity prices. The solvent

is regenerated during low electricity prices dropping down to 0.12 and CO2 is accumulated in

the solvent during high electricity prices with increased loading up to 0.235. The DoC (red

line) varies within the day, however the IDoC is 90% at the end of the optimisation framework.

As it can be observed from Figure 13, rather than operating at a �xed

value, the lean loading varies with the electricity prices. With this operation,

the plant redirects less steam for solvent regeneration when electricity prices

are high, allowing the plant to increase pro�tability by selling more electricity.525

From the same �gure we can observe that the degree of capture (DoC) varies

within the day and drops down to 82% when the rate of solvent regeneration is

low (or the lean loading is high). However, the cumulative capture at the end

of the simulation is ∼ 90%. The total pro�t of this scenario is ¿503k, or 10.5%

more pro�table than the base case scenario.530

3.1.5. SCPC Scenario comparison

When comparing the various modes of �exible operation for the integrated

pulverised coal power plant and amine-based capture plant, there are several

33

things that need to be considered. In Figure, we present the cumulative costs

for the di�erent alternatives.535

Figure 14: Cost comparison of the various modes for �exible operation for the SCPC- The

grey column show the results for the exhaust gas venting scenario (EGV) for the constrained

case (IDoC is considered as an end point equality constraint �xed to 90%), the red column is

the varying solvent regeneration scenario (VSR) and the blue columns are the solvent storage

scenarios for 10,000 m3 tanks with and without CAPEX. In all cases, we �nd that time varying

solvent regeneration is the most pro�table option for providing additional �exibility to the

coal-�red power plant.

For the solvent storage (SS) scenario, the capital cost of the storage tanks

is an important factor that needs to be taken into account, as this contribution

makes the di�erence between storage being pro�table or not - particularly for

large volumes. In addition, the rate at which the storage volume was discounted

made a substantial di�erence to the pro�tability of this scenario. This is partic-540

ularly evident in the case of a storage tank of 10,000 m3, where an increase in

pro�t of 3.3% relative to the base-case was observed, one the cost had been paid

o�. Interestingly, this distinction was reduced as storage volume was decreased.

The data are presented graphically in Figure 14 and tabulated in Table 3.

34

Table 3: Integrated Degree of Capture (IDoC) and cumulative pro�t-SCPC

Scenario IDoC (%) Pro�t (k¿)

Load following 90 450

Exhaust gas venting (¿70 /tonCO2) 90 471

Variable solvent regeneration 90 503

Solvent storage (with CAPEX) 90 440

Solvent storage (no CAPEX) 90 465

A key conclusion of our study so far is that none of the modes of �exible545

operation will compromise the carbon intensity of the power plant, and in fact

have the potential to reduce it, depending on the control strategies employed.

Further, the di�erent options have the potential to enhance the pro�tability

of the power plant, by allowing it to exploit price volatility in the electricity

market.550

3.1.6. Sensitivity analysis SCPC

Traditionally, the main factors that a�ect the pro�ts accrued by a power

plant are the revenue from the increased power production during peak hours,

the cost related to the carbon price and the fuel price. The increased deploy-

ment of intermittent renewable energy has two principle e�ects: to increase the555

volatility (or peakiness) of electricity market and to reduce the important of

fossil fuel prices in setting wholesale electricity prices. As has already been ob-

served in Europe, a high penetration of intermittent renewable energy has the

potential to produce negative electricity prices in addition to very high electric-

ity prices [31]. It is therefore essential to explore the pro�t sensitivity to these560

price oscillations. In the ensuing sensitivity analysis, carbon prices vary from 0

to 150 ¿/tCO2and the electricity price di�erential (di�erence between high and

low electricity price (PD)) varies from negative (¿-80/MWh) to extreme high

values (¿180/MWh) to take into account the potential volatility in a future

electricity market. From Figure 15, we can observe that for negative electric-565

35

ity price di�erential and up to ¿25/MWh, and regardless of the carbon price,

there is a reduction in pro�t compared to the base scenario (PD= ¿45/MWh

and CO2 price =¿70/ton). As the electricity price di�erential increases then we

observe an increase in pro�t which increases monotonically with the increase at

the electricity price di�erential and carbon price. However, beyond a certain570

level of PD, the carbon price ceases to signi�cantly in�uence the pro�tability.

This trend is similar for all scenarios considered. We can also observe that the

position of the �star", which represents the base case can show the % di�erence

in pro�t between the di�erent scenarios (4.5%, 3.4% and 10.5% for the EGV,

SS and VSR, respectively). Moreover, for high electricity price di�erential, the575

increase in pro�t for the di�erent scenarios becomes more obvious.

36

Figure 15: Sensitivity analysis for the UK scenario with fuel price of ¿7.7/MWh. In this �gure

we illustrate the variation of CO2 prices and electricity price di�erential (PD) (di�erence

between low and high electricity prices) as a function of the cumulative pro�t (k¿) compared

to the central scenario (PD= ¿45/MWh and CO2 price =¿70/tonCO2). For high carbon

prices and negative or low PD (less than ¿45/MWh ) we observe negative pro�ts. As the

price di�erential increases then the gain increases and for high PD can reach more than ¿900k

for the most pro�table VSR scenario. The �star" represents the base case with CO2 price=¿70

tonCO2and PD= ¿45/MWh.

It is, of course, important to note that we didn't give the CCS plant the

option of either shutting down or storing its energy during periods of negative

electricity prices. This was done to mediate the e�ect of paying the intermittent

renewable energy sources to "spill" their power - in other words, thermal power580

plants will have to accept a loss during these periods. In the case of a shut

down, this is likely to incur a cost of approximately ¿250k per shut-down cycle.

Therefore the thermal plant would need to evaluate the trade-o� associated with

37

accepting this one time cost in addition to the additional maintenance costs and

reduced equipment lifetimes associated with more frequent shut-down cycles vs.585

the prospect of running at a loss during periods of negative prices.

3.2. CCGT model

Globally, natural gas is becoming more important as an energy vector. Im-

portantly, CCGT plants are often employed in a mid-merit role in the electric-

ity system where they provide a peaking and load-following service. Thus, they590

may be well suited to �exible operation when combined with CCS. Indeed, here,

they may enjoy two advantages over their coal-�red counterparts, namely their

greater e�ciency and lower carbon intensity. Thus, in this section we present

the results of our optimisation problem, the CCGT-CCS plant presented in

Sections 2.3 and 2.4.595

3.2.1. Load following

As for the coal-�red power plant, our base-case is a simple load-following

operation. We simulate the same behaviour in terms of load factor, ramp rates

and electricity prices for both scenarios. In Figure 16 we present the cumulative

pro�t accrued by the CCGT for a range of CO2 prices. It is evident from600

Figure 16 that the CCGT pro�t is approximately 50% of that of the coal plant

for the central scenario. The primary driver for this is that we have assumed

UK-type gas prices of ¿24.53/MWh which are approximately 3 times greater

than the coal price of ¿7.7/MWh. However, as the carbon intensity of the

CCGT is substantially less than that of the coal-�red power plant, both the605

costs associated with residual CO2 emissions and CO2 transport and storage are

less on a per MWh basis. However, it is interesting to compare the sensitivity to

carbon price exhibited in �gure 7 to that presented in �gure 16. In the case of

the SCPP, increasing the CO2 price from ¿70/tCO2 resulted in a 20% reduction

in pro�t whereas the same change in CO2 prices reduced the CCGT pro�ts by610

16%. In other words, CCGTs would appear to be less sensitive to CO2 prices

than coal �red power plants.

38

Figure 16: In this �gure we illustrate impact that varying CO2 prices has on the cumulative

pro�t. The results of this sensitivity analysis show that the carbon price is a signi�cant

in�uence on the total pro�t and can lead to 4% increase for low CO2 price to 16% pro�t

decrease when the CO2 prices are increased to ¿200/tCO2 .

3.2.2. Solvent storage

The results of this optimisation problem are presented in Figure 17. As

can be observed, there are two periods of solvent storage during periods of615

high electricity prices (06:00-10:00 and 16:00-19:00) and four periods of solvent

regeneration, with higher regeneration at the low electricity prices (¿55/MWh)

and lower regeneration at electricity prices of ¿100/MWh.

39

Figure 17: Split fraction vs electricity prices for solvent storage scenario. The split fraction

determines if the solvent is regenerated or stored. negative split fraction shows regeneration

during low electricity prices, while positive split fraction shows storage during low electricity

prices. For intermediate prices (¿70/ton CO2) we have ∼ 50 % regeneration

The solvent storage pro�les are presented in 18. As can be observed from

Figure 18, for a price of ¿70/tCO2(central scenario), during periods of high620

electricity prices the lean tank (black line) is emptying while the rich tank

(black dashed line) is �lling, as there is no regeneration. The opposite can be

observed for periods with low electricity prices.

40

Figure 18: Storage tanks pro�le during the day for di�erent CO2 prices. As can be observed

from this �gure, when the electricity prices are lowest then the lean solvent storage tank is

�lling up (black line). During higher electricity prices the solvent bypasses the regenration

process and is sent to the rich solvent storage tank which is �lling up (black dashed line).

Similarly to the SCPC plant, we have calculated the cumulative pro�t for

di�erent storage tank sizes for 3 and 10 years payback period. The results are625

presented in �gure 19.

41

Figure 19: Pro�t margin between the load following base case scenario and the solvent storage

scenario with and without the capital cost of the storage tanks for di�erent storage tank

sizes. This �gure shows a comparison between the base case scenario (black column) and

the solvent storage scenario with capital cost with 3 years payback period (grey columns),

10 years payback period (blue columns) and without capital cost (red columns) for di�erent

solvent storage sizes. For the case where solvent storage cost is considered, as the capital cost

of the storage tanks decreases with the size, the cumulative pro�t increases for the 3 years

payback period, while the opposite is observed for 10 years payback period. However, after

the capital cost has been paid o� (red columns) there is an increase to the cumulative pro�t

proportionally to the size of the tanks.

3.2.3. Exhaust gas venting

For the exhaust gas venting scenario, we have performed two simulations

with CO2 prices of ¿10/tCO2 and ¿70/tCO2 , as it is illustrated in Figure 20.

For a CO2 price of ¿70/tCO2, venting is not selected, and the IDoC is 89.9630

% (end point constraint of the optimisation). For a CO2 price of ¿10/tCO2,

during periods of high electricity prices (¿100/MWh) between 6:00-10:00 and

16:00-19:00, 33 % of the CO2 is vented decreasing the DoC at these times at

57 %. However during the other time periods the DoC reaches values of 99.9

%, so at the end of the day the IDoC is 89.9 % (end point constraint of the635

optimisation). For the SCPC plant the CO2 price that venting is selected is

42

¿10/tCO2 , similarly to the CCGT plant. However, the amount that is vented is

more for the CCGT (33%) compared to 21% for the SCPC, which is explained

by the higher carbon intensity of the SCPC plants compared to CCGT plants.

Figure 20: Results for the exhaust gas venting scenario for CCGT. The black line is the vent

fraction for a CO2 price of ¿70/tCO2. CO2 is vented during high electricity prices and extra

regeneration is performed during lower electricity prices to keep the carbon intensity in a

speci�c level

3.2.4. Variable solvent regeneration640

As for the SCPC case, here the strategy is to allow CO2 to accumulate in the

working solvent during periods of high electricity prices, and more deeply regen-

erate it at other times. The results of this optimisation problem are presented

in Figure 21 .

43

Figure 21: Solvent regeneration as a function of time and electricity price for CCGT. As

opposed to operating at a constant loading the lean loading varies with variable electricity

prices. When the electricity prices are high, the lean loading increases and the DoC drops to 84

% and the opposite with low electricity prices. This operation allows CO2 to be accumulated

in the solvent during peak electricity prices and the pro�t increases due to higher electricity

revenue.

As can be observed from this �gure, the value of the lean loading varies645

with the di�erent electricity prices. During periods with low electricity prices

(¿55/MWh), the lean loading is reduced from 0.23 to 0.19 for the �rst period and

in the last period it is set back to the starting point of 0.23. During periods with

high electricity prices the lean loading increases up to 0.25, to allow the plant

to direct less steam to solvent regeneration and increase the pro�t from selling650

the additional electricity. The DoC varies through the course of the simulation

and reaches a minimum value of 84% during periods with high electricity prices.

However, the IDoC at the end of the simulation is 90 %.

3.2.5. Scenario comparison

In this section we compare the performance of the three scenarios based on655

the cumulative pro�t attained by the plant for each mode of operation, taking

44

into account that the �exible operation.

Figure 22: Cumulative pro�t for the three scenarios for CCGT plant. The solid black column

presents the base case scenario. The grey column presents the results for the exhaust gas

venting scenario for ¿70/tCO2 . The red column is the variable solvent regeneration scenario

(VSR) and the blue column is the solvent storage scenario with and without CAPEX.

As can be observed from Figure 22, the exhaust gas venting scenario is 6 %

more pro�table than the conventional scenario at a carbon price of ¿70/tonCO2

and this pro�t increases as the carbon price is reduced. The time varying660

solvent regeneration scenario is the most pro�table scenario, by 13% more than

the load following scenario. The solvent storage scenario, is 11 % less pro�table

than the base case scenario, since the revenue associated with the electricity

selling cannot outweigh the capital cost of the storage tanks. After the capital

payback, the solvent storage scenario has a pro�t of ¿232k, which makes it 8665

% more pro�table than the base case scenario. If we compare these results

with those from the SCPC we observe the same trend: VSR>EGV>Base case

>Solvent storage. The results for the carbon intensity and cumulative pro�t for

the di�erent scenarios are summarised in table 4.

45

Table 4: Integrated Degree of Capture (IDoC) and cumulative pro�t-CCGT

Scenario IDoC (%) Pro�t (k¿)

Load following 90 213

Exhaust gas venting (¿70 /tonCO2) 90 230

Variable solvent regeneration 90 245

Solvent storage (with CAPEX) 90 190

Solvent storage (no CAPEX) 90 232

Comparing the behaviour of the CCGT and SCPC plants, we observe that670

the % pro�ts for all the scenarios are higher for the CCGT compared to the

SCPC plant, which shows that the proposed �exible strategies can provide more

pro�t for the CCGT plants.

3.2.6. Sensitivity analysis CCGT

Similarly to the SCPC plant, we have performed a sensitivity analysis on675

carbon and electricity prices, in order to show their impact on the results. As it

can be observed from Figure 23, negative electricity prices give negative pro�ts

for any carbon price, whereas for electricity price di�erential of ¿180/MWh, the

pro�t increases more than ¿420k for the VSR scenario . The trend between the

di�erent scenarios is similar and follows similarly monotonic behaviour, while680

for each case the pro�t di�erence is (6%, 13% and 8% for the EGV, VSR and

SS scenarios, respectively)

46

Figure 23: Sensitivity analysis for the UK scenario with fuel price=24.53 £/MWh. In this

�gure we illustrate the variation of CO2 prices and electricity price di�erential (PD) (di�erence

between low and high electricity prices) as a function of the pro�t compared to the central

scenario (PD= ¿45/MWh and CO2 price =¿70/ton). The �star" represents the base case

with CO2 price=¿70 /tonCO2/ and PD= ¿45/MWh. For high carbon prices and negative

or low PD (up to 45) we observe negative pro�ts. As the price di�erential increases then the

pro�t increases. For the VSR scenario the pro�t increases more than ¿420k for high electricity

prices.

4. Conclusions

We have presented integrated models of both SCPC and CCGT power plants,

each integrated with a post-combustion amine-based CO2 capture plant, mod-685

elled using the gCCS toolkit. We have used this model to evaluate the pro�tabil-

ity of a decarbonised power plant operating �exibly by considering four di�erent

scenarios: conventional load following scenario, solvent storage, �ue gas venting

47

and variable solvent regeneration. We have formulated an optimisation prob-

lem in order to evaluate the pro�tability and carbon intensity for each of the690

scenarios. For the load following scenario,the SCPC exhibits higher pro�ts due

to lower fuel prices compared to the CCGT. When comparing the SCPC with

the CCGT plant for the USA, wherein gas prices of ¿7.17/MWh and coal prices

of ¿3.5/MWh , consistent with current low US gas and coal prices, we observe

that the pro�t of the CCGT increases to ¿435k as opposed to ¿533k for the695

SCPC plants, which indicates that the CCGT plants become competitive with

coal plants, with the potential to displace them from their position in the merit

order. Similar trends were observed for all the scenarios. For the exhaust gas

venting scenario, the IDoC constraint is very important and strongly a�ects the

plant's pro�tability. For the CCGT plant the % venting is higher due to the700

lower carbon intensity of the plant. For the solvent storage scenario, where the

capital cost of the storage tanks is considered, the revenue from the electricity

prices cannot outweigh the capital cost. However, after the payback period the

solvent storage scenario has 3.4% and 8% extra pro�t for the SCPC and CCGT,

respectively for the 10,000 m3 case. The e�ect of the capital cost on the results705

is very important, and needs to be considered when designing a new integrated

power-capture system with �exible operation. Moreover, the payback period is

another factor that needs to be taken into account when considering the solvent

storage option, since the results showed that for 10 years payback period the

solvent storage scenario is more pro�table than the base case scenario. The710

most pro�table option for both the SCPC and CCGT plants is the variable sol-

vent regeneration scenario for the di�erent carbon and fuel prices. This �exible

operation can increase the pro�tability of both the SCPC and CCGT plant by

10.5% and 13 %, respectively compared to the base case, while maintaining the

carbon intensity at 90%. respectively. This study has examined the various715

�exible operational modes of an integrated PCPP and CCGT plant with post-

combustion capture for di�erent electricity price di�erential (PD) and di�erent

CO2 prices. The SCPC plants seems to be more sensitive to increases in CO2

prices than gas and the CCGT plants exhibits higher increase in pro�ts than

48

the PCPP plant, showing that �exible operation is more pro�table for CCGT720

plants. We can therefore with con�dence draw the conclusion that for both

SCPC and CCGT plants the variable solvent regeneration scenario is the most

pro�table and least carbon intensive option.

5. Acknowledgements

The authors gratefully acknowledge the �nancial support of the grant EP/M001369/1725

MESMERISE-CCS.

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