Fuel 331 (2023) 125695
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Fuel
journal homepage: www.elsevier.com/locate/fuel
Multi-timescale and control-perceptive scheduling approach for flexible
operation of power plant-carbon capture system
Han Xi a, Mingjuan Zhu a, Kwang Y. Lee b, Xiao Wu a, *
a
b
National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, China
Department of Electrical and Computer Engineering, Baylor University, One Bear Place #97356, Waco, TX 76798-7356, USA
A R T I C L E I N F O
A B S T R A C T
Keywords:
Solvent-based carbon capture
Coal-fired power plant
Multi-timescale scheduling
Control-perceptive scheduling
Flexible operation
A carbon-capture system is required for coal-fired power plants to meet environmental regulation and provide
support for grid penetration of renewable energy sources, when the integrated power plant-carbon capture
system must operate in a highly flexible manner to accommodate the need for intermittent renewable power. To
this end, this paper proposes a hierarchical scheduling scheme for the operation of integrated power plant-carbon
capture system to fully exploit the decarbonization and flexibility of carbon capture technology in multiple
timescales. The upper layer of the hierarchy implements a day-ahead stochastic scheduling to provide hourly
operating instructions in the next 24 h so that the overall economic performance of the plant can be best ach­
ieved, including fuel consumption, load following and carbon trading. A novel index is then introduced to reflect
the power adjustment contribution of carbon capture in the intraday scheduling layer and enhance the load
ramping performance of power plant by flexible operation of the post-combustion carbon capture in minute
timescale. In the lower layer of the hierarchy, control-perceptive scheduling based on close-loop dynamic model
is proposed to coordinate the scheduling and underlying control, making the scheduling feasible for the oper­
ating practice. In addition, a new control system, which uses the reboiler steam flowrate to control the CO2
production rate and solvent circulation flowrate to control reboiler temperature is proposed to achieve superior
flexibility support role of carbon capture. The case study shows that the proposed scheduling approach can
improve the load tracking performance by 36.7% with satisfactory performance in carbon capture, which verifies
the efficacy of the proposed scheduling approach in the power system. The impact of the proposed control system
is also investigated, providing a broad insight on the flexible operation of carbon capture technology integrated
with the power plant.
1. Introduction
The CO2 emission from power sector reached a record high of 14.1 Gt
in 2019, accounting for over 41 % of global emissions according to the
statistical data published by the International Energy Agency (IEA) [1].
While greatly promoting the development of renewable energy sources
(RESs), retrofitting existing fossil fuel-fired power plants (FFPPs) with
carbon capture technologies is also a direct solution to alleviate the
carbon emissions [2]. The reason is the high share of power generation
from fossil fuels due to the continual rising of power demand [3]. This
trend is particularly evident in developing economies since most of the
coal-fired power plants (CFPPs) were built in recent years and could run
for another three to four decades [4].
Solvent-based post-combustion carbon capture (PCC) has been
confirmed as the most mature and feasible carbon capture technology
for the FFPPs [5]. However, it has a lower energy return ratio consid­
ering the high operating and investment costs [6]. For PCC technology,
much effort has been made on developing advanced solvent [7] and
modifying process configurations [8]. Utilization of CO2 has also drawn
much attention, offering different pathways to convert the captured CO2
into valuable fuels, chemicals, and materials [9].
With the increasing penetration of RESs in the power grid, the CFPPs
need to shift its operating mode from undertaking the base load to load
following frequently over a wide range to ease the integration of inter­
mittent RESs. Such a functional transformation of CFPP brings both
challenges and opportunities for the integrated PCC system. On the one
hand, the PCC must adapt to the strong fluctuations in flue gas due to the
load variations of the CFPP [10]. On the other hand, if the steam used for
solvent regeneration can be properly withdrawn from the steam turbine,
* Corresponding author.
E-mail address: wux@seu.edu.cn (X. Wu).
https://doi.org/10.1016/j.fuel.2022.125695
Received 10 May 2022; Received in revised form 7 August 2022; Accepted 17 August 2022
Available online 26 August 2022
0016-2361/© 2022 Elsevier Ltd. All rights reserved.
H. Xi et al.
Fuel 331 (2023) 125695
Nomenclature
Symbols
mCO2
CCFPP
CPCC
CCO2
Cimb
Abbreviations
IEA
International Energy Agency
FFPPs
Fossil Fuel-fired Power Plants
CFPPs
Coal Fired Power Plants
RES
Renewable energy source
PCC
Post-combustion Carbon Capture
CCUS
Carbon capture, Utilization and Storage
PV
Photovoltaic
MEA
Monoethanolamine
LP
Low pressure steam turbine
Index
L1
L2
L3
t
s
Day-ahead scheduling
Intraday scheduling
Control perceptive dynamic scheduling
Index of time
Index of scenarios
Symbols
PCFPP
Power output of CFPP without integration with PCC, MW
PQPCC
PCFPP−
PCPCC
PPV
Prate
PV
PWT
Prate
WT
Pbuy
Pcut
Pload
mcoal
mfg
mstPCC
mscf
mpro
CO2
PCC
αcoal
αCO2
αbuy
αcut
βopex
PCC
βopex
CFPP
ηCFPP
ηLP
ηPV
QLHV
coal
μcut
ΔhLP
φfg
CL
Treb
TSTC
GSTC
The power loss due to reboiler steam extraction of PCC,
MW
Power output of integrated CFPP-PCC system, MW
Power consumptions of PCC for solvent circulation and
CO2 compression, MW
Power output of PV, MW
Rated power output of PV, MW
Power output of wind turbine, MW
Rated power output of wind turbine, MW
The purchased power, MW
The curtailment of renewable power, MW
Power load demands, MW
Mass flowrate of pulverized coal, kg/s
Mass flowrate of flue gas, kg/s
Mass flowrate of reboiler steam, kg/s
Mass flowrate of solvent circulation, kg/s
Mass flowrate of CO2 production, kg/s
Tc
ν
νr
νci
νco
GT
σ
J
πs
γ
Np
Ns
the PCC is able to enhance the flexibility of the upstream CFPP to
manage deeper and faster load following [11]. As a result, carbon cap­
ture can become beneficial for accommodating RES in the grid, while the
excess power from RESs can also be fully used to reduce the operating
cost of carbon capture [12]. The flexible operation of integrated coalfired power plant-post-combustion carbon capture (CFPP-PCC) system
thus has great potential in the context of high penetration of RESs.
In-depth studies are required to understand the potentials and dif­
ficulties of the CFPP-PCC system, starting from dynamic modeling to the
optimal scheduling and real-time control [2]. First principle models [13]
and models identified with data-driven approaches [14] were developed
to gain knowledge of the interactions between the PCC and CFPP, and
followed by the design of conventional [15] and advanced controllers
[16], which make the PCC to better adapt to the flue gas variation [17]
and run in a power-carbon cooperative manner with the upstream CFPP
[18].
Recently, many efforts have also highlighted the value of flexible
operation in scheduling the PCC systems under changing power demand
and electricity price. Husebye et al. [19] evaluated the flexibility of PCC
in a market with cyclical electricity price based on solvent regeneration
and storage models, where storage tank levels were optimized to
maximize the weekly profit, showing a positive correlation between the
optimal storage tank level and electricity price. A better economic
Mass flowrate of carbon emission, kg/s
Operating cost of CFPP, $
Operating cost of PCC, $
CO2 trading cost, $
Penalties on the imbalance between electricity supply and
demand, $
Unitary price of coal, $/kg
Unitary trading price of CO2, $/kg
Electricity price, $/MWh
Penalty coefficient of renewable electricity curtailment,
$/MWh
Maintenance cost of CFPP, $/MWh
Maintenance cost of PCC, $/kg CO2
Power generation efficiency of CFPP
Power generation efficiency of the LP turbine
Power attenuation coefficient of PV
Low calorific value of coal, kJ/kg
CO2 emission factor of coal, kg/kg
Enthalpy drop of LP, kJ/kg
CO2 concentration of flue gas, wt.%
CO2 capture level, %
Reboiler temperature, K
Temperature of PV array under standard test condition, K
Insolation at the surface of PV array under standard test
condition, kW/m2
Temperature of PV array, K
Wind speed, m/s
Rated wind speed, m/s
Cut-in wind speed, m/s
Cut-out wind speed, m/s
Insolation at the surface of PV array, kW/m2
Carbon quota allocation of CFPP
Objective function
Occurrence probability of scenario s
Power adjustment contribution rate of PCC
Rolling horizon of intraday scheduling
Rolling horizon of control perceptive dynamic scheduling
performance can be achieved by storing the solvent rich in CO2 during
the periods of high-electricity price and regenerating it during the lowelectricity price. Zaman and Lee [20] evaluated the economic perfor­
mance of a CFPP-PCC system under two flexible operating modes,
including variant capture level and solvent storage. The results showed
that significant cost savings can be attained by implementing the two
flexible operating strategies. Flue gas bypass operation mode was also
studied for the PCC during the power plant start-up to alleviate the
impact of steam extraction on the turbine by venting partial flue gas to
the atmosphere [21]. Mac Dowell and Shah [22] presented another
operation mode of PCC called time-varying solvent regeneration, which
exhibited superior daily economic performance in the scheduling
compared with other operating modes. In fact, all these operating modes
share a common idea of changing the energy consumption of PCC
flexibly according to the load demands and market conditions. Alie et al.
[23] compared the scheduling results of PCC process under flexible and
fixed CO2 capture level requirements. A reduced-order model with fewer
variables was used in the optimal scheduling instead of the original
high-fidelity Aspen Plus® model to reduce the computational
complexity. The results showed that, flexibly changing the CO2 capture
level can markedly enhance the economic performance of the integrated
system by providing more reserve power, although the CO2 emission is
slightly higher than the fixed capture level case. Khalilpour [24]
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H. Xi et al.
Fuel 331 (2023) 125695
Fig. 1. The schematic diagram of a regional power system consisting of CFPP-PCC and RESs.
developed a multi-period mixed integer linear program (MILP) to opti­
mize the operation of PCC trains integrated with CFPP under the pro­
jected carbon and electricity markets. The results showed that, the
operating profit can be increased by ramping down or even shutting
down the PCC process during the periods with high electricity price.
Generally, the scheduling only provides the process with the optimal
operating points, and the tracking of these operating points is still
dependent on the underlying control system. To this end, Arce et al. [25]
presented an optimal operation framework for the solvent regeneration
system of PCC. The upper layer calculated the hourly-based optimal CO2
production by solving an economic scheduling problem. Lower control
layer was then designed to regulate the plant dynamically towards the
operating point issued by the scheduling layer. Abdul Manaf et al. [26]
developed similar optimization framework for an integrated CFPP-PCC
system, in which economic scheduling was applied to find the future
power output and CO2 capture level set-points; while model predictive
control (MPC) was implemented in the control layer to achieve fast
tracking of the scheduling results.
All the aforementioned studies indicate the advantages of flexible
scheduling to improve the economic performance of the PCC system.
Some studies also gave insights into the mutual benefits between the
PCC and RESs through effective scheduling. Chen et al. [12] carried out
the scheduling study of a low carbon energy system consisting of su­
percritical CFPP-PCC, wind turbines and photovoltaic panels. They used
artificial intelligence approaches of deep belief neural network [27] and
particle swarm optimization [28] for model development and sched­
uling optimization. The results showed that by flexible operation of PCC,
51 % more renewable power can be accommodated in the grid to pro­
vide 35 % of the energy required for carbon capture. Similar results were
also observed by Zhang and Zhang [29] through scheduling of an
electricity-heat-gas energy mix integrated with carbon capture, utiliza­
tion and storage. Both of these studies expounded the views on the low
carbon transformation of the energy system that there should be a
symbiotic relationship between PCC and renewable power rather than a
competitive one.
Although the scheduling of CFPP-PCC system has been extensively
studied, all of them focused on the day-ahead scheduling, which aims to
find the hourly optimal operating points of power generation and CO2
capture in the next day. Although the CFPPs are required to follow the
load frequently in real time, such a scheduling cannot deal with the
unavoidable uncertainties of day-ahead predictions of electricity price,
load demand or renewable generation [30]. In addition, the hourly
scheduling is unable to make full use of the flexibility of carbon capture
to manage faster load ramping of the power plant in a smaller timescale,
and provide more dispatchable energy to the power grid. Moreover,
because simplified steady-state model representing the couplings among
fuel, electricity and CO2 emission are often used, the day-ahead sched­
uling cannot consider the closed-loop dynamic control performance of
the CFPP-PCC system. The lack of coordination between scheduling and
control could be a threat to the economic or even stable operation of the
integrated plant, because the set-points issued by the scheduling can be
infeasible to the underlying control [31]. These limitations make the
standalone day-ahead scheduling method impossible to be directly used
in the real operation of integrated CFPP-PCC system. Therefore, it is
imperative to develop a comprehensive scheduling framework, which is
closely related to the control practice and is capable of exerting the
decarbonization and flexibility of carbon capture in multiple timescales.
For these reasons, this paper proposes a novel hierarchical sched­
uling structure consisted by three layers of scheduling with different
timescale features to achieve an economic, flexible and low carbon
operation of the integrated CFPP-PCC system. The upper layer imple­
ments a day-ahead stochastic scheduling, in which hourly operating
instructions are optimized to enhance the overall economic performance
of the plant involving fuel consumption, electricity load following and
carbon trading in the entire next day. The middle layer conducts a
rolling horizon intraday scheduling to modify day-ahead power outputs
according to the ultra-short-term forecast data. A novel index reflecting
the power adjustment contribution of PCC is introduced to improve
power ramping performance of CFPP by promoting the flexible opera­
tion of PCC in a small timescale. The lower layer offers a controlperceptive close-loop dynamic scheduling to further refine the
intraday scheduling results, so that more feasible results can be issued to
underlying control. To our best knowledge, this paper is the first study to
propose a complete and practical scheduling scheme for the integrated
CFPP-PCC system to fully exploit the decarbonisation and flexibility of
carbon capture. Case study using real data of weather condition and load
demand demonstrates that the proposed scheduling scheme can improve
the power load tracking performance by 36.7 % with satisfactory per­
formance in carbon capture. Further discussions are also presented to
identify the roles of each scheduling layer and to understand the influ­
ence of underlying control on the scheduling. The novel contributions of
this study are as follows:
3
H. Xi et al.
Fuel 331 (2023) 125695
Fig. 2. The diagram of 660 MW CFPP-PCC model [32].
1) A hierarchical scheduling scheme is proposed for the integrated
CFPP-PCC system to fulfill the decarbonization and flexibility sup­
port functions of PCC in multiple timescales;
2) A novel index reflecting power adjustment contribution of PCC is
introduced in the scheduling to offer additional dispatchable power
for the CFPP by promoting the flexible operation of PCC in a small
timescale;
3) A control-perceptive scheduling is developed to bridge the gap be­
tween the optimality of the scheduling and the feasibility of the
underlying control;
4) A novel PCC control system is proposed in coordination with
scheduling scheme, which can further improve the power ramping
performance of the connected CFPP;
5) The superiority of proposed scheduling scheme is demonstrated
based on real-time operating simulation. Comprehensive discussions
are also presented to identify the roles of each scheduling layer.
power output of the CFPP. Therefore, developing a model that can well
reflect the relationships between coal, heat, power and carbon within
the CFPP-PCC system is the prerequisite to the optimal scheduling study.
Considering that optimal scheduling based on a detailed firstprinciple model is highly complex and time consuming, a surrogate
model which focuses on the interactions between key variables of the
CFPP-PCC will be developed first.
Based on the energy conservation principle, the power output of
standalone CFPP can be calculated by:
2. System description and model development
mfg = ffg (mcoal )
(2)
To validate the effectiveness of the proposed scheduling approach in
accommodating the intermittent RESs, a regional power grid is consid­
ered as shown in Fig. 1. The regional power demand is met by a su­
percritical CFPP-PCC plant and RESs of wind turbines and photovoltaic
(PV) arrays. Given the lack of additional adjustable energy devices, it is
of great significance to exploit the flexibility of the integrated CFPP-PCC
system in meeting the supply–demand as fast as possible in the context
of uncertain load demands and intermittent RESs.
φCO2 = μCO2 mcoal /mfg
(3)
(1)
PCFPP = ηCFPP mcoal QLHV
coal
where mcoal is the mass flowrate of pulverized coal;
is the low
calorific value of coal; ηCFPP denotes the power generation efficiency of
CFPP.
The flue gas flowrate mfg and CO2 concentration φCO2 can be esti­
mated by:
QLHV
coal
where ffg is the function between coal flowrate and flue gas flowrate;
μCO2 is the CO2 emission factor of coal, which is constant since the coal
characteristic is assumed to be unchanged.
For solvent regeneration in the PCC, some of the steam has to be
withdrawn from the inlet of low pressure (LP) steam turbine and sent to
the reboiler. As a result, significant power loss PQPCC occurs for the CFPP,
which can be calculated by:
2.1. Coal-fired power plant integrated with solvent-based carbon capture
plant
PQPCC = ηLP ΔhLP mstPCC
(4)
mstPCC
where
is the reboiler steam flowrate extracted from the steam
turbine; ΔhLP and ηLP are the enthalpy drop and power generation effi­
ciency of the LP turbine, respectively.
Consequently, the power output of integrated CFPP-PCC system
PCFPP− PCC can be given by:
The schematic structure of a 660MWe supercritical CFPP integrated
with 30 % monoethanolamine (MEA)-based PCC process is shown in
Fig. 2. There are strong interactions between the CFPP and PCC systems
because of the interconnections at the flue gas and water-steam systems.
The variations of flue gas flowrate during the load ramping of CFPP
brings a crucial effect on the operation of PCC, while the change of
reboiler steam flowrate for solvent regeneration in the PCC alters the
PCFPP−
PCC
= PCFPP − PQPCC
(5)
The following model is used to reflect the operating performance of
4
H. Xi et al.
Fuel 331 (2023) 125695
circulation flowrate; mstPCC is the reboiler steam flowrate, which can
reflect the heat consumption of PCC.
The electric power consumption caused by solvent circulation and
CO2 compression is directly provided by the regional power grid, which
can be calculated by:
PCPCC = a × mscf + b × mpro
CO2
where a and b are unitary power consumptions for solvent circula­
tion and CO2 compression, respectively.
Fig. 3 illustrates the feasible operation region of the integrated CFPPPCC system. The allowable operation range of standalone CFPP is rep­
resented by line E0-A0, which is shifted down to line E-A after the
integration with PCC due to the reboiler steam extraction. The feasible
operation region of retrofitted CFPP (A-B-C-D-E-A) is thus defined by
boiler rated load (BRL) condition (line A-B), 50 % BRL condition (line DE) and the specified minimum and maximum reboiler steam flow con­
ditions (lines A-E and B-C). In addition, the minimum LP steam flowrate
requirement (line C-D) should be met for the safe operation of the LP,
that the increase of reboiler steam flowrate must be achieved by
increasing the BRL. Line 1-5 and line 2-3 respectively represent mini­
mum (40 %) and maximum (95 %) CO2 capture level conditions at
designed reboiler temperature (392.2 K). With further consideration of
the limitation on minimum solvent circulation flowrate (line 4-5), the
feasible operation region of the integrated CFPP-PCC can represented by
the area1-2-3-4-5-1. Line 1′ -5′ and line 2′ -3′ denote the 50 % and 90 %
CO2 capture level conditions, respectively. The main operating param­
eters of these corner points are listed in Table A1.
Given the structure of the surrogate model, corresponding model
parameters are then identified based on the operating data generated
from a first-principle CFPP-PCC model previously developed in gCCS®
platform [33]. The inputs and outputs of the surrogate models are given
in Table 1. A total of 2500 steady-state samples covering the feasible
region are generated by simulating the first-principle CFPP-PCC model,
in which 2000 samples are used for parameter identification through
linear regression while the rest are used for validation. Part of the
validation results are shown in Fig. 4, which demonstrate that the sur­
rogate model is in good agreement with the first-principle model for
most variables within the feasible operating region. Therefore, the sur­
rogate model can be used in the scheduling optimization to improve the
Fig. 3. Feasible operation region of the integrated CFPP-PCC system.
Table 1
The inputs and outputs of the surrogate models.
CFPP-PCC
model
CFPP
PCC
Inputs
Coal mass flowrate (mcoal )
Feed water flowrate
Reboiler steam flowrate
(mstPCC )
Main steam valve position
Outputs
Power output (PCFPP− PCC )
Main steam pressure
Flue gas flowrate (mfg )
Flue gas composition (φfg )
Solvent circulation rate (mscf )
Reboiler steam flowrate
(mstPCC )
CO2 capture level (CL)
Re-boiler temperature (Treb )
CO2 production rate (mpro
CO2 )
the PCC process:
(
)
(
)
st
CL, Treb , mpro
CO2 = LPCC mscf , mPCC , mfg , φfg
(7)
(6)
where CL,Treb , mpro
CO2 are the CO2 capture level, reboiler temperature
and CO2 production rate of the PCC, respectively; mscf is the solvent
Fig. 4. Verification results of the surrogate model (red solid line: first-principle model; blue dashed line: surrogate model). (For interpretation of the references to
colour in this figure legend, the reader is referred to the web version of this article.)
5
H. Xi et al.
Fuel 331 (2023) 125695
Fig. 5. Proposed multi-timescale scheduling framework for the integrated CFPP-PCC.
computational efficiency. The data used in model identification and
verification can be found in the Supplementary Material.
3. Multi-timescale hierarchical scheduling of the integrated
CFPP-PCC system
2.2. Renewable power generations
To maximize the decarbonization and flexibility of carbon capture
technology and achieve an optimal coordinated operation of the inte­
grated CFPP-PCC system, a multi-timescale hierarchical scheduling
scheme is set up as shown in Fig. 5. The scheduling hierarchy is
composed by three layers with different timescales. The upper layer of
the hierarchy implements a day-ahead stochastic scheduling, which
minimizes the overall operating costs of the CFPP-PCC within the next
24 h by providing the hourly optimal operating instructions according to
the forecasts of renewable generations and power demands. The middle
layer of the hierarchy carries out a rolling horizon intraday scheduling to
modify the corresponding day-ahead scheduling results for the next 4 h
at a 15-minute interval. Because the intraday short-term predictions of
renewable generation and load demand are much more accurate than
the day-ahead prediction, the operating economics of the CFPP-PCC can
get improved in this layer. In addition, a novel index reflecting the
power adjustment contribution of the PCC is introduced in this layer to
promote the flexible operation of PCC in a small timescale, which is
useful to upgrade the power ramping performance of the CFPP. The
lower layer of the hierarchy offers a closed-loop dynamic scheduling to
bridge the gap between the optimality of the scheduling and the feasi­
bility of the underlying control. Finally, the intraday scheduling results
are further refined in a minute-scale and issued to the real time control.
RESs of wind turbines and solar PV are considered in the regional
power system. The power output of wind turbine can be calculated by
[34]:
⎧
0, v < vci ; v⩾vco
⎪
⎪
⎪
⎪
⎨
(v)3 − (vci )3
, vci ⩽v⩽vr
PWT = Prate
(8)
WT ×
⎪
(vr )3 − (vci )3
⎪
⎪
⎪
⎩
Prate
WT , vr < v < vco
where PWT and Prate
WT are the real power output and rated capacity of
wind turbine;v,vr , vci and vco are wind speed at the height of the turbine
hub, rated wind speed, cut-in and cut-out wind speeds, respectively.
The power output of PV panel can be calculated by [35]:
PPV = ηPV Prate
PV
GT
[1 + αp (TC − TSTC )]
GSTC
(9)
where PPV and Prate
PV are the real power output and the rated capacity
of PV array, respectively; ηPV is the power attenuation coefficient; GT and
TC are the insolation and temperature at the surface of PV array; GSTC
and TSTC are the insolation and temperature of standard test condition;
αp is the power-temperature coefficient.
3.1. Day-ahead stochastic scheduling
The upper layer of the hierarchy carries out a day-ahead economy6
H. Xi et al.
Fuel 331 (2023) 125695
(
s
= αCO2
CCO
2
24
∑
1 ,s
mLCO
(t) − σ
2
t=1
24
∑
)
1 ,s
PLCFPP−
PCC (t)
(13)
t=1
1 ,s
where mLCO
(t) is the carbon emission of integrated CFPP-PCC system;
2
αCO2 is unitary trading price of CO2 and σ denotes the carbon quota
allocation [38] of CFPP.
The supply–demand imbalance penalty is calculated by:
s
Cimb
= αcut
24
∑
PLcut1 ,s (t) +
24
∑
t=1
1 ,s
αbuy PLbuy
(t)
(14)
t=1
1 ,s
where PLbuy
(t) and PLcut1 ,s (t) are respectively the amount of purchased
electricity from external grid and the curtailment of renewable power at
time t under scenario s; whereas αcut and αbuy are the curtailment penalty
coefficient and electricity price, respectively.
The following constraints should also be satisfied under each sce­
nario in the day-ahead scheduling. First is the power balance constraints
of the regional power system given by:
Fig. 6. Power output responses corresponding to step changes of coal mass
flowrate and reboiler steam flowrate.
1 ,s
PLCFPP−
oriented scheduling, in which hourly operating instructions of the CFPPPCC within the next day are optimized according to the forecast of RES
generations and load demands. Given that the scheduling interval is one
hour, which is much longer than the closed-loop dynamic response time
of the process, the steady-state model of the CFPP-PCC developed in
Section 2 is applied in this layer to represent the interactions among
coal, heat, power, and carbon. Besides, scenario-based anti-uncertainty
approach is used in the scheduling to alleviate the impact of unavoidable
day-ahead forecasting error. Many possible scenarios of renewable
generations and load demands are generated to reflect the forecast un­
certainties using hyper-Latin sampling [36]. Then the simultaneous
backward scenario reduction scheme [37] is used to select several
representative scenarios from the initial scenario set to ease the
computational burden.
The following objective function is considered in the day-ahead
scheduling to minimize the daily operating costs of the integrated sys­
tem JL1 under all representative scenarios:
S
∑
minJL1 = min
d L1
dL1
+
]
24
∑
(
)
L1 ,s
1 ,s
αcoal mLcoal
(t) + βopex
CFPP PCFPP (t)
(10)
(11)
t=1
where αcoal is the unitary price of coal and βopex
CFPP is the unitary
maintenance cost of CFPP.
The operation costs of PCC can be calculated by:
s
CPCC
=
24
∑
(
pro,L1 ,s
βopex
(t)
PCC mCO2
)
(16)
L1 ,s
Treb
(t) = 392.2K
(17)
The middle layer of the hierarchy implements intraday scheduling of
the CFPP-PCC in a rolling horizon optimization manner [39]. For every
15 min, the intraday scheduling refines the day-ahead scheduling results
of the next four hours into 15-min resolution according to the intraday
short-term forecast of load demands and RES generation. The objective
of this layer is to alleviate the impact of day-ahead prediction error and
provide more dispatchable power in small timescale to ease the inte­
gration of intermittent renewable power.
Considering that the time interval of intraday scheduling is short­
ened to 15 min, it is necessary to consider the dynamic response of
power output to improve the load ramping performance of the CFPP.
Fig. 6 shows the responses of power output corresponding to step
changes of coal mass flowrate and reboiler steam flowrate. It can be
observed that the power output is mostly influenced by the coal mass
flowrate, but it takes more than 8 min to reach a new steady state
because of the inertia and time delay lumped in the processes of coal
pulverizing, combustion, and heat and mass transfer, etc. In contrast,
reboiler steam flowrate has a much quicker influence on power output,
and the response time is found to be less than 1 min. The results indicate
that flexible regulation of the reboiler steam can effectively improve the
power ramping performance of CFPP, and thus provide more dis­
patchable power in short timescale.
To promote the participation of PCC in power plant load ramping, a
novel index γL2 reflecting the power adjustment contribution of PCC is
proposed in the intraday scheduling:
in which dL1 is the decision variables of the day-ahead scheduling
1
including the power output of the integrated CFPP-PCC PLCFPP−
PCC and the
CO2 capture levelCLL1 ; CsCFPP and CsPCC stand for the operating costs of
CFPP and PCC, respectively; CsCO2 is the CO2 trading cost and Csimb rep­
resents the penalties on the imbalance between supply and demand. The
index s denotes the s-th scenario considered in the optimization, whereas
π s is the occurrence probability of scenario s.
The operating costs of CFPP are composed by the fuel and mainte­
nance costs:
s
CCFPP
=
50\% ⩽CLL1 ,s (t)⩽90%
3.2. Intraday scheduling
(
L1
PCC ; CL
(15)
is the load demands at time t under scenario s.
where
To ensure an efficient operation of the PCC, the following constraints
on CO2 capture level and reboiler temperature are also considered in the
optimization in addition to the feasible operating region of CFPP-PCC
given in Fig. 3.
s
s
s
πs CCFPP
+ CCO
+ CPCC
2
)
[ 1
, dL1 = PLCFPP−
L1 ,s
L1 ,s
1 ,s
PC,L
PCC (t) − Pcut (t) = Pload (t)
1 ,s
(t)
PLload
s=1
s
Cimb
L1 ,s
L1 ,s
L1 ,s
PCC (t) + PPV (t) + PWT (t) + Pbuy (t) −
(12)
t=1
γ L2 =
where βopex
PCC is the unitary maintenance costs of PCC.
The CO2 trading costs are presented by:
2
2
ΔPQ,L
ΔPQ,L
PCC
PCC
=
L2
L2
2
ΔPCFPP− PCC
ΔPCFPP + ΔPQ,L
PCC
2
ΔPLCFPP−
=
7
2
(PLCFPP
2
= PLCFPP−
− PL 1
( PCC L2 CFPP− LPCC
)
L1
1
PCFPP ) + − (PPCC − PPCC
)
2
2
= ΔPLCFPP
+ ΔPQ,L
PCC
PCC
−
(18)
(19)
H. Xi et al.
Fuel 331 (2023) 125695
Like the day-head scheduling, the following power balance con­
straints also need to be satisfied at intraday scheduling stage:
2
where ΔPLCFPP−
PCC is the adjustment of intraday power load instruc­
tion of CFPP-PCC based on the corresponding day-ahead instruction,
L2
2
and ΔPQ,L
PCC and ΔPCFPP denote the power adjustment contributions of PCC
reboiler steam and coal mass flowrate, respectively. A large value of γ
represents a great contribution of PCC to the power ramping in a short
timescale.
The following objective function is then used in the intraday
scheduling:
(
)
]
[ 2
L2 ,t0
L2
L2 ,t0
minJLt02 = min θ1 Cimb
− θ2 Cpac
, dL2 = PLCFPP−
PCC ; CL
dL2
2
PLCFPP−
⎪
⎪
⎪
⎪
⎪
⎩
L2 ,t0
Cpac
=
t∑
0 +NP
(20)
where dL2 is the decision variables of the intraday scheduling
2
including the power output of the integrated CFPP-PCC PLCFPP−
PCC and the
L2 ,t0
L2
CO2 capture levelCL ; Cimb is the intraday supply–demand imbalance
penalty within the future horizon starting from t0 + 1 to t0 + Np (Np = 16
in this study, since the next four hours with the time interval of 15 min is
2 ,t0 reflects the power adjustment
considered at each sampling time t0); CLpac
contributions of the PCC within the next four hours; θ1 and θ2 are cor­
responding weighting coefficients.
Considering that division between variables is contained inγL2 (t), the
objective function (20) cannot be directly solved by conventional linear
solver. It is thus rewritten into the following form to improve the
computational efficiency:
(
)
]
[ 2
L2 ,t0
L2
L2 ,t0
minJLt02 = min θ1 Cimb
, dL2 = PLCFPP−
+ θ2 Cpac
PCC ; CL
(23)
− ΔCLmax ⩽CLL2 (t + 1) − CLL2 (t)⩽ΔCLmax
(24)
50% − ε⩽CLL2 ⩽90% + ε
(25)
L2
Treb
(t) = 392.2K
(26)
where ε is the relaxation value. When ε is set to 0, the capture level
issued from day-ahead scheduling must be strictly followed in the
intraday stage.
Because the main variables within the CFPP-PCC can approach the
steady-state within 15 min, the steady-state model developed in Section
2 can still be used in the intraday scheduling, and the feasible operating
region given in Fig. 3 has to be considered as additional constraints.
At each sampling time, by minimizing the objective function (21)
subject to the above constraints, the best instruction sequence for the
power output and CO2 capture level within the given horizon Np can be
determined. However, only the current instructions are issued to the
lower layer of the hierarchy. The optimization will repeat at next sam­
pling period using the latest forecast data of RES generations and power
demands.
dL2
(21)
⎪
t∑
t∑
0 +NP
0 +NP
⎪
⎪
L2 ,t0
2
2
⎪
⎪
ΔPLCFPP
(t) + λ2
ΔPQ,L
⎩ Cpac = λ1
PCC (t)
t=t0 +1
L2
L2
max
− ΔPmax
CFPP ⩽PCFPP (t + 1) − PCFPP (t)⩽ΔPCFPP
where
and ΔCLmax are respectively the maximum ramping
power and CO2 capture level for the CFPP and PCC within the sched­
uling interval.
The CO2 capture level constraints given in the day-ahead scheduling
is relaxed as follows in the intraday scheduling to better unlock the
power adjustment role of PCC; whereas the same reboiler temperature
constraint is still required to be met.
γL2 (t)
⎧
t∑
t∑
0 +NP
0 +NP
⎪
L2 ,t0
⎪
2
⎪
Cimb
= αcut
PLcut2 (t) +
αtbuy PLbuy
(t)
⎪
⎪
⎨
t=t0 +1
t=t0 +1
(22)
ΔPmax
CFPP
t=t0 +1
dL2
L2
L2
2
PC,L
PCC (t) − Pcut (t) = Pload (t)
The rate constraints of CFPP-PCC need to be added in intraday stage
due to a short scheduling time interval:
dL2
⎧
t∑
t∑
0 +NP
0 +NP
⎪
L2 ,t0
⎪
2
⎪
PLcut2 (t) +
αtbuy PLbuy
(t)
⎪ Cimb = αcut
⎪
⎨
t=t0 +1
t=t0 +1
L2
L2
L2
PCC (t) + PPV (t) + PWT (t) + Pbuy (t) −
3.3. Control-perceptive dynamic scheduling
t=t0 +1
where λ1 and λ2 (λ1 > λ2) are the weighting coefficients to promote
the participation of PCC in power load regulation.
In the lower layer of the hierarchy, the time interval of scheduling is
further shortened to 5 min or smaller, so that the CFPP-PCC can provide
Fig. 7. Open-loop responses of PCC corresponding to the step changes of solvent circulation flowrate and reboiler steam flowrate.
8
H. Xi et al.
Fuel 331 (2023) 125695
Fig. 8. Schematic diagrams of the conventional and proposed control structures of PCC. (a) solvent circulation flowrate to control CO2 capture level, reboiler steam
flowrate to control reboiler temperature, (b) reboiler steam flowrate to control CO2 production rate, solvent circulation flowrate to control reboiler temperature.
more flexible support for the grid to handle the real time fluctuations on
both the RES and power load. Within such a small time period, the dy­
namic transition for most concerned variables cannot be finished.
Therefore, the steady-state model-based scheduling approach is chal­
lenging to provide optimal and feasible instructions in practice.
To this end, a control-perceptive dynamic scheduling is proposed in
this layer, in which closed-loop dynamic model of the CFPP-PCC is
applied to bridge the gap between the optimality of the scheduling and
the feasibility of control. The closed-loop dynamic model of the CFPPPCC can be described in the following form based on the first princi­
ple model developed in [32]:
dx/dt = f (x, u)
y = h(x, u)
u = g(r − y)
s.t. umin ⩽u⩽umax
− Δumax ⩽Δu⩽Δumax
closed-loop system model (27) can fully consider the performance of the
controller and estimate the dynamic variations of manipulated and
controlled variables according to the scheduling instructions.
Considering that the first-principle dynamic model (27) is compli­
cated and time-consuming for the minute-scale dynamic optimization,
the following discrete state-space model is identified and applied in the
control-perceptive scheduling to improve the computational efficiency:
{
z(t + 1) = Az(t) + Br(t)
[y(t); u(t)] = Cz(t) + Dr(t)
]
[ L3 ,r
pro,L3 ,r
L3 ,r
(t); Treb
(t)
r(t) = PCFPP−
PCC (t); mCO2
[ L3
]
L3
L3
3
y(t) = PCFPP− PCC (t); mpro,L
CO2 (t); Treb (t); CL (t)
[
]
L3
3
3
u(t) = mLcoal
(t); mst,L
PCC (t); mscf (t)
(27)
(28)
where A, B, C, D are the parameters of state-space model; z(t) is the
state variables of the state-space model at time t, which do not have
specific physical meaning but is used to reflect the dynamic relationship
pro,L3 ,r
3 ,r
between input and output; PLCFPP−
(t) are the scheduling
PCC (t) and mCO2
where × denotes the internal state variables of integrated CFPP-PCC,
such as the steam temperature in the heat exchangers and the CO2
loading in the solvent; u stands for manipulated variables, such as coal
mass flowrate, solvent circulation flowrate and reboiler steam flowrate;
y denotes the controlled variables, such as power output, CO2 produc­
tion rate and reboiler temperature; umin, umax and Δumax are magnitude
and rate limitations of u; f and h denote the first-principle functions of
CFPP-PCC; g stands for the feedback controller function which calculates
u based on the deviation between y and the scheduling instruction r. The
L3 ,r
(t) is the
instructions of power output and CO2 production rate; Treb
reboiler temperature. The selection of scheduling instructions of the PCC
process in this layer is dependent on the control system development,
which will be introduced in the next section.
Based on the dynamic model (28), the objective function of controlperceptive scheduling is given by:
9
H. Xi et al.
Fuel 331 (2023) 125695
[ 3 ,r
L3 ,t0
L3 ,t0
L3 ,t0
minJL3 = min(θ3 Cimb
+ θ4 Cpac
+ θ5 Ctra
), dL3 = PLCFPP−
dL3
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
dL3
L3 ,t0
Cimb
= αcut
t∑
0 +Ns
t=t0 +1
L3 ,t0
Cpac
= λ3
t∑
0 +Ns
t∑
0 +Ns
PLcut3 (t) +
PCC (t)
]
3
αtbuy PLbuy
(t)
t=t0 +1
3 ,r
ΔPLCFPP
(t) + λ4
t=t0 +1
⎪
t∑
0 +Ns (
⎪
⎪
L3 ,r
L3 ,t0
⎪
⎪
PCFPP−
⎪ Ctra = ω1
⎪
⎪
t=t0 +1
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
pro,L3 ,r
PCC ; mCO2
t∑
0 +Ns
3 ,r
ΔPQ,L
PCC (t)
(29)
t=t0 +1
3
− PLCFPP−
)2
PCC (t)
+ ω2
t∑
0 +Ns
(
)2
3 ,r
3
mpro,L
(t) − mpro,L
CO2 (t)
CO2
t=t0 +1
+ω3
t∑
0 +Ns
(
L3 ,r
L3
Treb
(t) − Treb
(t)
)2
t=t0 +1
50% − ε⩽CLL3 ⩽90% + ε
(31)
On the other hand, Fig. 7 also shows that the CO2 production rate can
be quickly influenced by the reboiler steam flowrate. This motivates us
to select the CO2 production rate as controlled variable and regulate it
through the reboiler steam flowrate. The CO2 production rate can also
serve as an indicator to evaluate the performance of CO2 capture.
Moreover, under such a design, the CO2 production rate instruction is­
sued by the scheduling can lead to a rapid response of reboiler steam
flowrate, which is helpful to exploit the flexibility of PCC in a small
timescale to upgrade the power ramping performance of the CFPP.
For these reasons, a novel control structure as visualized in Fig. 8(b)
is developed in the proposed scheduling scheme. The CO2 production
rate and reboiler temperature are selected as controlled variables, and
the reboiler steam flowrate and solvent circulation rate are selected as
the corresponding manipulated variables to adjust them. To improve the
response ability of the PCC to flue gas and ensure an efficient operation
of absorber, flue gas feedforward is added in the proposed control
structure to speed up the adjustment of solvent circulation flowrate.
L3 ,r
Treb
(t) = 392.2K
(32)
4. Results
3 ,t0
3 ,t0
Like the intraday scheduling, CLimb
and CLpac
are considered in the
objective function to reduce the supply–demand imbalance penalty in 5minute timescale and promote the PCC to participate in power adjust­
ment. Besides,CLtra3 ,t0 , the dynamic tracking offset between scheduling
instructions and controlled variables within the future horizon starting
from t0 + 1 to t0 + Ns is also included to reflect the feasibility of the
scheduling results; ω1, ω2 and ω3 are corresponding weighting co­
efficients. The control-perceptive scheduling aims to optimize the
operating performance of the CFPP-PCC within the next one hour at the
time resolution of 5 min, thus Ns is set as 12 in this layer.
In addition to the feasible region given in Fig. 3, the following con­
straints are also imposed on the scheduling optimization to ensure
power balance and safe operation of the PCC process:
3 ,r
PLCFPP−
L3
L3
L3
PCC (t) + PPV (t) + PWT (t) + Pbuy (t) −
L3
L3
3
PC,L
PCC (t) − Pcut (t) = Pload (t)
(30)
Rolling horizon optimization strategy will also be applied in this
layer using the latest forecast data of RES generations and power
demands.
This section verifies the effectiveness of the proposed scheduling
scheme through simulation study of a 660MWe super-critical CFPP in­
tegrated with 30 % MEA-based PCC in an integrated energy mix. Besides
3.4. Underlying control
Since the lower layer scheduling is designed with control perceptive,
the dynamic performance of underlying PCC control system will affect
the scheduling results. Therefore, the open-loop dynamic responses of
CO2 capture level, reboiler temperature and CO2 production rate are
investigated with +5 % step changes of reboiler steam flowrate and
solvent circulation flowrate respectively, which are shown in Fig. 7. It
can be found that reboiler steam flowrate has a very slow influence on
the CO2 capture level, and the transient time to a new steady state is
more than 2.5 h. Meanwhile, solvent circulation flowrate also shows a
slow effect on reboiler temperature. The reason for these two features
can be explained by the large time constant caused by solvent holdup in
packing and buffer tank. Given that solvent circulation flowrate shows
an instantaneous effect on CO2 capture level, conventional control
structure of PCC [16,26], as shown in Fig. 8(a), manipulates the solvent
circulation flowrate to achieve fast adjustment of CO2 capture level,
whilst maintaining the reboiler temperature by changing the reboiler
steam flowrate. However, such a control structure requires continuous
adjustment of reboiler steam flowrate to compensate for the slow in­
fluence induced by the solvent circulation rate, which is easy to cause
long-term fluctuations in power generation from the perspective of the
integrated CFPP-PCC system.
Fig. 9. Uncertain predictions and representative scenarios of renewable power
and load demands (light grey line: uncertain predictions; colored line: repre­
sentative scenarios considered in the day-ahead scheduling).
10
H. Xi et al.
Fuel 331 (2023) 125695
Table 2
Economic parameters for the scheduling of integrated CFPP-PCC system.
Parameters
Units
Values
Coal costs
Maintenance costs of CFPP
Renewable power curtailments penalty
CO2 trade costs
carbon quota allocation
Electricity purchasing prices
$/ton
$/MWh
$/MWh
$/ton
tCO2/MWh
$/MWh
140 [40]
5.73 [12]
80
25 [41]
0.3
83.7 [42]
Table 3
Weight coefficients in scheduling.
Parameters
Values
Parameters
Values
θ1
θ2
θ3
θ4
θ5
10
2
10
2
2
5
λ1
λ2
λ3
λ4
4
0.05
10
0
20
1
ω2
ω1
ω3
Fig. 10. Day-ahead scheduling results of the CFPP-PCC system.
generated and sent to the PCC absorber. As a result, more steam is
withdrawn from the turbine for solvent regeneration to maintain the
given day-ahead CO2 capture level. Such an action reduces the power
generation in turn, making the PCC a stumbling block for the flexible
operation of the CFPP. Therefore, as observed in the bottom right figure
of Fig. 11, the power adjustment contribution of the PCC is negative
most of the time. This feature is more prominent in case of large dayahead prediction error, because more power adjustments are required
to be made by the CFPP.
The proposed intraday scheduling scheme also modifies the dayahead scheduling instructions, trying to reduce the RES curtailment
and electricity purchase in this layer. However, since the power
adjustment contribution of the PCC is considered in the objective
function of intraday scheduling, the PCC is actively changing the
reboiler steam to support the power ramping of the CFPP. The bottom
left figure of Fig. 11 illustrates that around 25 % of the power adjustment
task in the intraday scheduling is contributed by the PCC throughout the
day. In case of small day-ahead prediction error, PCC can complete most
of the power adjustment task, which will reduce the load varying
operation difficulties of the upstream CFPP. Moreover, by imposing the
relevant constraint in the scheduling, the CO2 capture level is fluctuating
within a given range around the day-ahead scheduling instructions.
The scheduling results of the bottom layer hierarchy and real-time
dynamic operating performance of the CFPP-PCC for both conven­
tional and proposed approaches are presented in Figs. 12 and 13. The
key operating performance indices are listed in Table 4. As shown in
Fig. 13, different scheduling instructions are generated by the two
scheduling schemes, which lead to remarkable differences in the realtime operating performance. Without considering the dynamic oper­
ating practice of the CFPP-PCC system, the scheduling instructions is­
sued by the conventional approach cannot be well tracked by the
underlying control system. The variations of power output set-points are
poorly tracked by the control system within the scheduling interval.
Apparent steady-state offsets appear in cases of large set-point changes.
The supply–demand imbalance caused by the tracking error is up to
103.36MWh under the conventional scheduling approach throughout
the day, which equals to an additional cost of $2.8 k based on the eco­
nomic parameters considered.
By contrast, using the control-perceptive scheduling approach, the
load instructions issued by the proposed scheduling scheme can be well
tracked by the underlying control system. Moreover, the use of power
adjustment contribution index and control system matched with this
idea further upgrades the power ramping capability of the CFPP. The
supply–demand imbalance is reduced by 36.7 % compared with the
conventional approach, which means that more RES power can be
accommodated in the power grid.
the CFPP, 165 MWe wind power generation and 100 MWe PV power
generation are also included in the energy mix for power supply. Real
weather and load demands data derived from [12] are used in the
simulation. Fig. 9 shows the uncertain forecasts of wind power outputs,
PV power outputs and power demands for the next 24 h and the corre­
sponding representative scenarios selected through the simultaneous
backward scenario reduction [37].
Economic parameters and weight coefficient in scheduling are listed
in Tables 2 and 3, respectively. Large θ1 and θ3 are applied in the
scheduling because the prior target of CFPP is to eliminate the
demand–supply imbalance. We also set λ1 > λ2, λ3 > λ4 to improve the
initiative of PCC in power adjustment, and set a larger ω1 in the controlperceptive scheduling layer to ensure the feasibility of the scheduling
instructions.
The conventional PCC scheduling approach [22] is also applied in
the same day-ahead scheduling layer and compared with the proposed
approach. However, in the middle and lower layers of the scheduling,
only the CFPP changes the load according to the latest forecasts of RES
generations and power demands, and the PCC only follows the flue gas
variation of CFPP, maintaining the CO2 capture level according to the
instructions optimized in the day-ahead scheduling. Besides, steadystate model is used in all three scheduling layers, and the dynamic
operating practice is not considered in the scheduling. The scheduling
results and real time operation performance of the CFPP-PCC system
under the two scheduling approaches are shown in Figs. 10-13.
Fig. 10 shows the day-ahead scheduling results of the CFPP-PCC. It
can be observed that, during valley load periods, higher CO2 capture
level instructions are dispatched to the PCC, which enables the CFPP to
further lower the power output to accommodate more renewable power.
While during the peak load periods, the CO2 capture level is reduced so
that more steam can be used in the turbine for power generation. Even
so, 25.83MWh of electric power still needs to be purchased during
10:00–12:00 because of the high load demands and insufficient renew­
able power. In general, the economic operation of integrated system
within the next 24 h can be achieved through flexible change of CO2
capture level in an hourly-based timescale.
Based on the day-ahead scheduling, the intraday scheduling results
of the proposed and conventional scheduling schemes are compared in
Fig. 11. Both scheduling schemes refine the power output instructions of
CFPP to 15-minute timescale according to the latest forecast informa­
tion. For the conventional scheduling approach, the flexibility of PCC in
small timescale is not activated as it tightly follows the day-ahead cap­
ture level instructions. Most of the time, the power adjustment task of
CFPP is undertaken only through the variation of coal mass flowrate.
When CFPP needs to increase power output, more flue gas flowrate is
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H. Xi et al.
Fuel 331 (2023) 125695
Fig. 11. Intraday scheduling results of the CFPP-PCC system: (a) proposed scheduling scheme, (b) conventional scheduling scheme.
Fig. 12. Control-perceptive scheduling results of the (a) proposed scheme, and (b) conventional scheme.
The proposed scheduling scheme fully exploits the flexibility support
function of the PCC process in multiple timescales, which significantly
improves the load ramping performance of the CFPP. This improvement
allows the fluctuations of CO2 capture level during the operation.
However, it is worth noting that a little higher daily average CO2 capture
level is achieved in the case study under the proposed scheduling
scheme compared with the conventional scheme. This finding indicates
the importance of refining the scheduling instructions of CO2 capture
level according to the latest forecasts of RES generations and load de­
mands, because the accommodated RES power allows the PCC to
withdraw more steam from the CFPP to increase the CO2 capture level.
The case study illustrates that better load following and economic per­
formance of the CFPP-PCC can be achieved under the proposed sched­
uling scheme.
12
H. Xi et al.
Fuel 331 (2023) 125695
Fig. 13. Real-time dynamic operation performance of the CFPP-PCC under two scheduling schemes: (a) power output, (b) CO2 production rate, and (c) CO2 cap­
ture level.
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H. Xi et al.
Fuel 331 (2023) 125695
CFPP power regulation. As a result, the power adjustment contribution
rate of PCC is zero for most of the day, which indicates that all power
output modifications caused by the day-ahead forecast error are handled
by the CFPP independently. By contrast, with smaller λ2 and λ4 in the
scheduling, Cases 2 and 3 give higher power adjustment contribution
rates to the PCC, which effectively promote the PCC to flexibly change
the CO2 production rate to upgrade the power ramping ability of CFPP.
As can be observed from Fig. 15, Cases 2 and 3 achieve superior power
load tracking performance compared with Case 1 and reduce the dy­
namic tracking offset to 70.10MWh and 65.43MWh, respectively as
shown in Table 5. Besides, due to the zero mean normal distribution of
the forecast error [43], the adjustment of reboiler steam also presents
the similar trend for power output modification. Therefore, although the
instantaneous CO2 capture level is fluctuating over the scheduling pe­
riods, the daily average CO2 capture level is changed little.
Table 4
Key operating performance indices of proposed and conventional scheduling
schemes.
Real operation results
Conventional scheme
Proposed scheme
Purchased power (MWh)
Curtailed power (MWh)
Average capture level
CO2 emission (ton)
Coal consumption (ton)
Total costs (k$)
76.90
52.29
0.814
1773.4
5150.7
793.0
58.57
32.69
0.818
1767.2
5154.2
790.2
5. Discussion
The case study results have demonstrated the advantages of pro­
posed scheduling scheme for the economic and flexible operation of
integrated CFPP-PCC system. The following discussions are carried out
in this section to elaborate the benefits of the innovations proposed in
this study.
5.2. The effect of control-perceptive scheduling
To demonstrate the underlying control performance in the bottom
layer scheduling, scheduling results based on steady-state and close-loop
dynamic models are compared in Fig. 16. Because the control-perceptive
scheduling knows that the power ramping speed of CFPP is not that fast
during the scheduling interval due to the slow impact of coal mass
flowrate, the variations of power output instruction are smaller
compared with that issued by the steady-state scheduling. In contrast,
stronger variations of CO2 production rate instructions are issued by the
control-perceptive scheduling, since it can be rapidly regulated by the
reboiler steam flowrate, thus facilitates the use of carbon capture for
CFPP power ramping.
Nevertheless, the steady-state scheduling [12,23,24] ignores the
dynamic operating practice of the plant and assumes that the load
changes can be finished instantaneously. Consequently, infeasible in­
structions are issued, which cannot be followed by the control system
within the scheduling interval. Moreover, the fast impact of reboiler
steam flowrate on the power output cannot be fully exploited although
the power adjustment contribution rate of PCC has been considered in
the scheduling. As shown in Fig. 16, the scheduling result issued by the
control-perceptive scheduling can be more closely followed by the un­
derlying control. The supply–demand imbalance caused by dynamic
tracking offset is reduced by 12.07 MWh compared with the steady-state
scheduling.
5.1. The effect of power adjustment contribution rate index of PCC
The power adjustment contribution rate index of PCC is considered
in the intraday and control-perceptive scheduling layers to encourage
the use of PCC in CFPP load ramping. To demonstrate its effectiveness,
case studies of the proposed scheduling scheme under three different
index weighting coefficients are compared in Table 5. Small values of λ2
and λ4 represent strong preferences of using PCC in power ramping in
the intraday and control-perceptive scheduling layers, respectively.
The PCC power adjustment contribution rates under three cases are
shown in Fig. 14. Case 1 applies the biggest values of λ2 and λ4 in the
scheduling, which imposes severe penalty for the participation of PCC in
Table 5
Key operating performance indices of the scheduling under different power
adjustment contribution rates of PCC.
Cases
Value of λ
Tracking offset of power
output
Average CO2 capture
level
Case 1
λ2 = 10; λ4 =
10
λ2 = 10; λ4 = 0
λ2 = 0.05; λ4
=0
88.37 MWh
81.6 %
70.10 MWh
65.43 MWh
81.8 %
81.8 %
Case 2
Case 3
Fig. 14. The PCC power adjustment contribution rates in intraday and control-perceptive layer scheduling under three cases.
14
H. Xi et al.
Fuel 331 (2023) 125695
Fig. 15. Participation of PCC on (a) power output ramping performance and (b) set-points of CO2 production rate in three cases.
5.3. The superiority of proposed control structure in the scheduling
scheme
acceptable performance in the capture-level following. The goal to
enhance the power ramping performance cannot be focused in the
scheduling, which overshadows the flexibility of the PCC unit. Fig. 17
also illustrates that although CO2 capture level is not directly controlled
in the proposed PCC control system, it shows a highly synchronous
variational trend with the conventional control. Therefore, such a PCC
control system can better serve in the proposed scheduling scheme for
power adjustment to support the short-term RES accommodation.
All the scheduling schemes compared in Sections 4 and 5 are sum­
marized in Table 6, which quantitatively illustrate the superiority of the
proposed scheduling scheme.
The real-time operation performance of the CFPP-PCC under the two
control strategies visualized in Fig. 8 is compared in Fig. 17. The pro­
posed PCC control system uses reboiler steam flowrate to control the
CO2 production rate. The CO2 production rate instruction issued by the
scheduling can cause a rapid and smooth regulation of reboiler steam
flowrate, which is a benefit for the power adjustment of CFPP. There­
fore, the proposed control structure shows better power tracking per­
formance as can be observed in Fig. 17. On the other hand, the
conventional PCC control system [16,26] uses the solvent circulation
rate to control the CO2 capture level and the reboiler steam flowrate to
stabilize the reboiler temperature. The issued CO2 capture level in­
struction can cause a long-term variation of reboiler steam flowrate due
to the slow dynamics of the PCC. Such a feature causes the control dif­
ficulty for the CFPP, because the coal mass flowrate has to be continually
altered to compensate the influence of reboiler steam on the power
output. Therefore, poorer power ramping performance is observed with
the conventional PCC control system, which results in 11.16MWh higher
power tracking offset compared with the proposed control structure. In
addition, because the CO2 capture level is difficult to be controlled
within the 5-min scheduling interval, great concern on the CO2 capture
level has to be given in the control-perceptive scheduling to attain an
6. Conclusion
This paper proposes a complete and practical scheduling scheme for
the operation of integrated coal-fired power plant-carbon capture sys­
tem, in which three hierarchical scheduling layers with different time­
scales are incorporated to fully exploit the decarbonization and
flexibility of carbon capture technology. The upper layer of the sched­
uling hierarchy utilizes the flexibility of carbon capture on the hourly
scale to optimize the economic performance of the integrated plant in
the next 24 h; while the middle and lower layers exploit the flexibility of
carbon capture on minute timescale to unlock superior power ramping
capability of the power plant. Moreover, a control-perceptive scheduling
15
H. Xi et al.
Fuel 331 (2023) 125695
Fig. 16. Real-time operation results under dynamic scheduling and steady-state scheduling: (a) power output, (b) CO2 production rate.
16
H. Xi et al.
Fuel 331 (2023) 125695
Fig. 17. Real-time operation results under different PCC control structure: (a) power output, (b) CO2 capture level.
Table 6
The summary of comparison results.
Proposed
Conventional
Case 2 in Section 5.1
Case 3 in Section 5.1
Steady-state scheduling in
Section 5.2
Conventional control in Section
5.3
PCC power adjustment
contribution rate
Dynamic
scheduling
Control
structure
Tracking offset of power output
(MWh)
Average capture
level (%)
High
Not applied
Medium
Low
High
✓
×
✓
✓
×
Proposed
Conventional
Proposed
Proposed
Proposed
65.43
103.36
70.10
88.37
77.50
81.8
81.4
81.8
81.6
81.7
High
✓
Conventional
76.59
82.0
is developed for the lower layer to make the scheduling instructions
more feasible for the operating practice; and a novel PCC control system
is also presented to better support the power adjustment of CFPP in the
proposed scheduling framework. The case study results show that better
power ramping and economic performance can be achieved for the in­
tegrated CFPP-PCC system under the proposed scheduling framework.
On the premise of maintaining a satisfactory CO2 capture performance,
36.7 % of the supply–demand mismatch caused by power tracking offset
can also be eliminated, with 19.6 MWh of renewable power accommo­
dated over the 24 h. This paper points to the new scheduling approach
for the economic and flexible low carbon operation of the power plant-
carbon capture system in multiple timescale, which is beneficial for the
deployment of PCC technology in the context of increasing renewable
energy penetration.
CRediT authorship contribution statement
Han Xi: Conceptualization, Methodology, Investigation, Software,
Writing – original draft. Mingjuan Zhu: Methodology, Validation,
Software. Kwang Y. Lee: Writing – review & editing. Xiao Wu:
Conceptualization, Methodology, Validation, Writing – review & edit­
ing, Funding acquisition.
17
H. Xi et al.
Fuel 331 (2023) 125695
Declaration of Competing Interest
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The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
The authors would like to acknowledge the National Natural Science
Foundation of China (NSFC) under Grant 51976030, Natural Science
Foundation of Jiangsu Province for Outstanding Young Scholars under
Grant BK20190063, National Key R&D Program of China under Grant
2021YFE0112800, and EU H2020 Marie Skłodowska-Curie Research
and Innovation Staff Exchange Scheme under Grant 101007963 for
funding research into this work.
Appendix A
Table A1
Main operating parameters of the feasible operation region of CFPP-PCC.
Points
coal mass
flowrate
(kg/s)
reboiler
steam
flowrate
(kg/s)
power
output
(MW)
flue gas
flowrate
(kg/s)
solvent
flowrate
(kg/s)
CO2
capture
level
(%)
A0
E0
A
B
C
D
E
1
2
3
4
5
1′
2′
3′
4′
75.3
34.2
75.3
75.3
41.9
34.2
34.2
75.3
75.3
34.2
34.2
45.6
75.3
75.3
34.2
34.2
0.0
0.0
40.0
180.0
180.0
124.8
40.0
62.4
164.0
120.8
49.5
50.1
79.0
165.2
117.0
56.4
660.0
300.0
641.3
575.6
279.4
238.6
280.3
631.6
583.6
240.8
274.5
374.9
623.0
582.7
242.5
272.3
556.1
399.9
556.1
556.1
426.6
396.9
396.9
556.1
556.1
396.9
396.9
441.1
556.1
556.2
396.9
396.9
/
/
/
/
/
/
/
250.3
632.7
452.4
200.0
200.0
313.3
588.9
423.7
225.7
/
/
/
/
/
/
/
40.0
95.0
95.0
44.3
40.0
50.0
90.0
90.0
50.0
Appendix B. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.fuel.2022.125695.
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