IGCC Power Plant Design of Dispatch Capabilities
Ming-Wei Yang, Benjamin P. Omell and Donald J. Chmielewski
Illinois Institute of Technology
AIChE Annual meeting 2012
100 101 102 103 104 105 106 107 108 109
0
50
100
150
200
Time (days)
Rev
en
ue (
$1
00
0/h
r) Dispatch
"o Dispatch
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Outline
• Motivation and Objective
• Control of IGCC Dispatch Operation
• Equipment Design
• Case Study
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Integrated Gasification Combined Cycle
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Power System Operation
Gas Turbine
Coal Fired
Grid
DemandNuclear
Department of Chemical and Biological Engineering
Illinois Institute of Technology
IGCC Role: Conventional Wisdom
Gas Turbine
Coal Fired
Grid
DemandNuclearIGCC
Department of Chemical and Biological Engineering
Illinois Institute of Technology
IGCC with Dispatch Capabilities
Gas Turbine
Coal Fired
Grid
DemandNuclear
IGCC
Department of Chemical and Biological Engineering
Illinois Institute of Technology
IGCC With Dispatch
- Respond to Market Prices - Increase Average Revenue
Electricity Spot Price
Opportunity:
0 5 10 15 200
50
100
time (days)
Energy Value
($/MWhr)
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Process Modification for Dispatch Capabilities
Department of Chemical and Biological Engineering
Illinois Institute of Technology
IGCC with Throughput Manipulation
Manipulation
of Fuel Flow
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Hydrogen Gas Storage
Hydrogen
Gas
Storage
Assume Carbon
Capture Operations
Department of Chemical and Biological Engineering
Illinois Institute of Technology
IGCC with Compressed Air Storage
Compressed
Air
Storage
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Dispatch Opportunities
Gasification Block(Includes ASU Distillation,
Gasifier and Acid Gas Removal)
Power Block(Includes Expansion Turbine,
Combustion Turbine, HRSG,
and Steam Turbine)
ASUs,A
C
coal
s,H2H2
G
H2 Storage(MH2)
Compressed
Air Storage(MA)
MACASU Main
Air Compressor
PGPC
P�
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Dispatch Manipulations
Gasification Block(Includes ASU Distillation,
Gasifier and Acid Gas Removal)
Power Block(Includes Expansion Turbine,
Combustion Turbine, HRSG,
and Steam Turbine)
ASUs,A
C
coal
s,H2H2
G
H2 Storage(MH2)
Compressed
Air Storage(MA)
MACASU Main
Air Compressor
PGPC
P�
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Process Model
Gasification Block(Includes ASU Distillation,
Gasifier and Acid Gas Removal)
Power Block(Includes Expansion Turbine,
Combustion Turbine, HRSG,
and Steam Turbine)
ASUs,A
C
coal
s,H2H2
G
H2 Storage(MH2)
Compressed
Air Storage(MA)
MACASU Main
Air Compressor
PGPC
P�
322
221
/
/
βν
νββ
GHH
HCA
Pdt
dM
Pdt
dM
−=
−=
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Constrained Process Model
Gasification Block(Includes ASU Distillation,
Gasifier and Acid Gas Removal)
Power Block(Includes Expansion Turbine,
Combustion Turbine, HRSG,
and Steam Turbine)
ASUs,A
C
coal
s,H2H2
G
H2 Storage(MH2)
Compressed
Air Storage(MA)
MACASU Main
Air Compressor
PGPC
P�
max
max
max
22
max
22
max
0
0
0
0
0
GG
CC
HH
HH
AA
PP
PP
MM
MM
≤≤
≤≤
≤≤
≤≤
≤≤
νν 322
221
/
/
βν
νββ
GHH
HCA
Pdt
dM
Pdt
dM
−=
−=
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Outline
• Motivation and Objective
• Control of IGCC Dispatch Operation
• Equipment Design
• Case Study
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Controller Design Using Instantaneous Revenue
ePC
Economic ModelPredictiveControl
Economic LinearOptimal Control
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Economic Model Predictive Control
Maximize Average Revenue with Point-wise-in-time Constraints
0
min max
1max
. .
( )
T
e
x u
C P dtT
s t x Ax Bu Gw
z D x D u
z z zτ
= + +
= +
≤ ≤
∫&
• Braun, 1992 • Morris et al., 1994• Kintner-Meyer and
Emery, 1995• Henze et al., 2003
• Oldewurtel et al, 2010• Ma et al, 2012 • Mendoza-Serrano and
Chmielewski, 2012
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Literature on EMPC
• Conceptual Development and Stability Issues: Rawlings and Amrit (2009); Diehl, et al. (2011); Huang and Biegler (2011); Heidarinejad, et al. (2012)
• Process Scheduling: Karwana and Keblisb (2007); Baumrucker and Biegler (2010); Lima et al. (2011); Kostina et al. (2011)
• Power Systems: Zavala et al. (2009); Xie and Ilić (2009), Hovgaard, et al. (2011), Omell and Chmielewski (2011)
• HVAC Systems: Braun (1992); Morris et al. (1994); Kintner-Meyer and Emery (1995); Henze et al. (2003); Braun (2007); Oldewurtel et al. (2010), Ma et al. (2012); Mendoza and Chmielewski (2012)
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Economic Linear Optimal Control
{ }( )
0
2 min max
1max lim
. .
E[ ] min ,
T
e GT
x u
C P dtT
s t x Ax Bu Gw
z D x D u
z z z
→∞
= + +
= +
≤ −
∫&
Maximize Average Revenue with Statistical Constraints
• Yang, 2009• Yang, Omell, Chmielewski,
2011
• Yang, Omell, Chmielewski,2012
• Mendoza-Serrano andChmielewski, 2012
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Electricity Price Model
weC
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Electric Price Spectral Density
eCw
10-3
10-2
10-1
100
101
10-2
100
102
104
Frequence (rad/hr)
Sp
ectr
al
Den
sity
(($
/MW
)2/h
r)
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Electricity Electric Price Realization
eCw
100 101 102 103 104 105 106 107 108 109
70
80
90
100
110
Time (days)
Ele
ctr
icit
y V
alu
e (
$/M
W h
r)
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Scaled Electricity Prices
x Ax Bu Gwα= + +&
Overall Process Model:
Department of Chemical and Biological Engineering
Illinois Institute of Technology
min max
x u
x Ax Bu Gw
z D x D u
z z z
α= + +
= +
≤ ≤
&
1 2 2
22 3
max
max
2 2
max
2 2
max
max
/
/
0
0
0
0
0
AC H
HH G
A A
H H
H H
C C
G G
dMP
dt
dMP
dt
M M
M M
P P
P P
β β ν
ν β
ν ν
= −
= −
≤ ≤
≤ ≤
≤ ≤
≤ ≤
≤ ≤
Constrained State Space Model
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Stochastic Constraint
Lxu
uDxDz
GwBuAxx
ux
=
+=
++=&
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
L
...1,2 and 2
)()(
)()(0
such that Find
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Stochastic Constraints
*
Constraints:2 ii z<ζ
2z
1z
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
L
...1,2 and 2
)()(
)()(0
such that Find
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
Department of Chemical and Biological Engineering
Illinois Institute of Technology
[ ][ ]GGGGG
eeeee
PPPPP
CCCCC
=−=
=−=
, ~
, ~
ELOC Objective Function
GeGe
Ge
T
GeT
PCPCE
PCEdtPCT
+=
=
∫∞→
]~~
[
][1
lim0
where
Department of Chemical and Biological Engineering
Illinois Institute of Technology
ELOC Objective Function
GeGe
T
GeT
PCPCEdtPCT
+=
∫∞→
]~~
[1
lim0
Then, enforce the conditioneG CP~~
α=
Ce
e
eeGe
CE
CCEPCE
Σ=
=
=
α
α
α
]~[
])~
(~[]
~~[
2
Department of Chemical and Biological Engineering
Illinois Institute of Technology
ELOC Synthesis
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
ts
...1,2 and 2
)()(
)()(0
..
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
{ }GeCe
LPC
jjx
+Σ≥Σ
αα
σζ ,,,0,min
xLu ELOC=⇒
Department of Chemical and Biological Engineering
Illinois Institute of Technology
ELOC Synthesis
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
ts
...1,2 and 2
)()(
)()(0
..
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
{ }GeCe
LPC
jjx
+Σ≥Σ
αα
σζ ,,,0,max
xLu ELOC=⇒
Can be Solved with a Convex Optimization Problem
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Case Study Process Parameters
Adapted from NETL Baseline Report (Case 2)
- PNnom= 544MW PG
nom= 611MW PCnom = 67MW
- ννννH2nom = ννννGnom = 90 tonneH2/hrννννCnom = ννννASUnom = 790 tonne compressed air/hrννννcoal nom = 220 tonne coal/hr
- ββββ1 = 0.085 MW hr /tonne airββββ2 = 8.78 tonne air / tonneH2ββββ3 = 6.79 MW hr /tonneH2ββββ4 = 2.44 tonne coal /tonneH2
Department of Chemical and Biological Engineering
Illinois Institute of Technology
ELOC Example
Synthesis
Gas
Storage
MW 1222
H tonne705
max
2
max
2
=
=
G
H
P
M
2
Ce /MWhr)20($=Σ
Department of Chemical and Biological Engineering
Illinois Institute of Technology
ELOC Example
100 101 102 103 104 105 106 107 108 109
0
50
100
150V
alu
e (
$/M
W h
r)
100 101 102 103 104 105 106 107 108 109
0
500
1000
1500
Po
wer (
MW
)
100 101 102 103 104 105 106 107 108 109
0
500
1000
Time (days)
Ma
ss (
ton
nes)
Instantaneous
Average
Maximum
PG
Ce
MH2
Department of Chemical and Biological Engineering
Illinois Institute of Technology
ELOC Example
100 101 102 103 104 105 106 107 108 109
0
50
100
150
Time (days)
Revenue (
$1000/h
r)
Dispatch
"o Dispatch
Average Revenue without Dispatch= 48,000 $/hrAverage Revenue with Dispatch = 55,000 $/hrOverall increase of 14.6%
Department of Chemical and Biological Engineering
Illinois Institute of Technology
ELOC Example
Synthesis
Gas
Storage
MW 1222
H tonne705
max
2
max
2
=
=
G
H
P
M
2
Ce /MWhr)20($=Σ
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Outline
• Motivation and Objective
• Control of IGCC Dispatch Operation
• Equipment Design
• Case Study
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Equipment Size Variables
−−−−
−+Σmax
2
maxmax
22
max
max
222 ))((max
GHcAHHAA
HHHCef
PcPcMcMc
cPV ννα
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
ts
...1,2 and 2
)()(
)()(0
..
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Equipment Size Variables
−−−−
−+Σmax
2
maxmax
22
max
max
222 ))((max
GHcAHHAA
HHHCef
PcPcMcMc
cPV ννα
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
ts
...1,2 and 2
)()(
)()(0
..
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Equipment Size Variables
−−−−
−+Σmax
2
maxmax
22
max
max
222 ))((max
GHcAHHAA
HHHCef
PcPcMcMc
cPV ννα
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
ts
...1,2 and 2
)()(
)()(0
..
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Equipment Size Variables
−−−−
−+Σmax
2
maxmax
22
max
max
222 ))((max
GHcAHHAA
HHHCef
PcPcMcMc
cPV ννα
zjjjj
jj
T
j
T
uxxuxjj
T
w
T
xx
njzz
LDDLDD
GGSBLABLA
ts
...1,2 and 2
)()(
)()(0
..
minmax
2
=−<<
=
+Σ+=
++Σ+Σ+=
σσ
ζσ
ρρζ
α
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Outline
• Motivation and Objective
• Control of IGCC Dispatch Operation
• Equipment Design
• Case Study
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Case Study Economic Parameters
Adapted from NETL Baseline Report (Case 2)- cf = $33/tonne coal
- cG = $2.55x105/MW
cC = $1.04x106/MW
- cA = $35.2/tonne compressed air cH2 = $540/tonneH2.
- Interest Rate = 7%
Project Horizon = 30yrs
Average Price of Electricity = $90/MW hr
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Case 1 ΣΣΣΣCe = ($20/MWhr)2
Standard Deviation Electricity Price is $20/MWhr
*
2Hν
max*
AMmax*
2HM
max*
CP
max*
GP
= 90 tonneH2/hr,
= 0.0 tonne air
= 705 tonneH2
= 67 MW
= 1222 MW
Present value of $5.08x108 and
Revenue increase of 14.6%
Solution:
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Case 1: ΣΣΣΣCe = ($20/MWhr)2
100 101 102 103 104 105 106 107 108 109
0
50
100
150V
alu
e (
$/M
W h
r)
100 101 102 103 104 105 106 107 108 109
0
500
1000
1500
Po
wer (
MW
)
100 101 102 103 104 105 106 107 108 109
0
500
1000
Time (days)
Ma
ss (
ton
nes)
Instantaneous
Average
Maximum
PG
Ce
MH2
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Changes in Electric Price Variation
($/MWhr)2 (tonne/hr) (tonne) (MW) (tonne) (MW)
Present
Value
($x106)
Revenue
Increase
(%)
202 90 0.0 67 705 1222 508 14.6
CeΣ*
2Hνmax*AM
max*CP
max*2HM
max*GP
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Changes in Electric Price Variation
($/MWhr)2 (tonne/hr) (tonne) (MW) (tonne) (MW)
Present
Value
($x106)
Revenue
Increase
(%)
52 90 0.0 67 0.0 611 0.0 0.0
202 90 0.0 67 705 1222 508 14.6
CeΣ*
2Hνmax*AM
max*CP
max*2HM
max*GP
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Changes in Electric Price Variation
($/MWhr)2 (tonne/hr) (tonne) (MW) (tonne) (MW)
Present
Value
($x106)
Revenue
Increase
(%)
52 90 0.0 67 0.0 611 0.0 0.0
102 90 0.0 67 705 1222 175 7.3
202 90 0.0 67 705 1222 508 14.6
CeΣ*
2Hνmax*AM
max*CP
max*2HM
max*GP
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Changes in Electric Price Variation
($/MWhr)2 (tonne/hr) (tonne) (MW) (tonne) (MW)
Present
Value
($x106)
Revenue
Increase
(%)
52 90 0.0 67 0.0 611 0.0 0.0
102 90 0.0 67 705 1222 175 7.3
202 90 0.0 67 705 1222 508 14.6
402 90 0.0 67 705 1222 1172 29.3
452 90 6200 134 705 1222 1426 36.6
CeΣ*
2Hνmax*AM
max*CP
max*2HM
max*GP
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Case 5: ΣΣΣΣCe = ($45/MWhr)2
100 101 102 103 104 105 106 107 108 109-200
0
200
Va
lue (
$/M
W h
r)
100 101 102 103 104 105 106 107 108 109
0
500
1000
1500
Po
wer (
MW
)
100 101 102 103 104 105 106 107 108 109
0
2000
4000
6000
Time (days)
Ma
ss (
ton
nes)
Instantaneous
Average
MaximumC
e
PG
PC
MA
MH2
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Case 5: ΣΣΣΣCe = ($45/MWhr)2
100 101 102 103 104 105 106 107 108 109
0
50
100
150
Time (days)
Revenue ($1000/hr)
Dispatch
"o Dispatch
100 101 102 103 104 105 106 107 108 109
0
50
100
150
200
Time (days)
Revenue ($1000/hr) Dispatch
"o Dispatch
Case 1: ΣΣΣΣCe = ($20/MWhr)2
Case 5: ΣΣΣΣCe = ($45/MWhr)2
Department of Chemical and Biological Engineering
Illinois Institute of Technology
max
2Hν
2Hν
Why is Throughput Manipulation Not Selected?
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Conclusions
1. Three opportunities to make IGCC dispatchableinvestigated.
- H2 storage found to be most viable.
- Gasifier throughput found to be non-viable.
- Compressed air storage only viable under extreme price variability.
Department of Chemical and Biological Engineering
Illinois Institute of Technology
Acknowledgements
• Former Students and Collaborators:
Amit Manthanwar
Dr. Jui-Kun Peng (ANL)
Professor Javad Abbasian (ChBE, IIT)
• Funding:
National Science Foundation (CBET – 0967906)
Chemical & Biological Engineering Department, IIT