igcc power plant design of dispatch...

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


Outline

• Motivation and Objective

• Control of IGCC Dispatch Operation

• Equipment Design

• Case Study



Integrated Gasification Combined Cycle



Power System Operation

Gas Turbine

Coal Fired

Grid

DemandNuclear



IGCC Role: Conventional Wisdom

Gas Turbine

Coal Fired

Grid

DemandNuclearIGCC



IGCC with Dispatch Capabilities

Gas Turbine

Coal Fired

Grid

DemandNuclear

IGCC



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)



Process Modification for Dispatch Capabilities



IGCC with Throughput Manipulation

Manipulation

of Fuel Flow



Hydrogen Gas Storage

Hydrogen

Gas

Storage

Assume Carbon

Capture Operations



IGCC with Compressed Air Storage

Compressed

Air

Storage



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�



Dispatch Manipulations





and Steam Turbine)

ASUs,A

C

coal

s,H2H2

G

H2 Storage(MH2)

Compressed

Air Storage(MA)

MACASU Main

Air Compressor

PGPC

P�



Process Model





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

−=

−=



Constrained Process Model





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

−=

−=



Outline




• Case Study



Controller Design Using Instantaneous Revenue

ePC

Economic ModelPredictiveControl

Economic LinearOptimal Control



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



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)



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



Electricity Price Model

weC



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)



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)



Scaled Electricity Prices

x Ax Bu Gwα= + +&

Overall Process Model:



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



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



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



[ ][ ]GGGGG

eeeee

PPPPP

CCCCC

=−=

=−=

, ~

, ~

ELOC Objective Function

GeGe

Ge

T

GeT

PCPCE

PCEdtPCT

+=

=

∫∞→

]~~

[

][1

lim0

where



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



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=⇒



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



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



ELOC Example

Synthesis

Gas

Storage

MW 1222

H tonne705

max

2

max

2

=

=

G

H

P

M

2

Ce /MWhr)20($=Σ



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



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%



ELOC Example

Synthesis

Gas

Storage

MW 1222

H tonne705

max

2

max

2

=

=

G

H

P

M

2

Ce /MWhr)20($=Σ



Outline




• Case Study



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



Outline




• Case Study



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



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:



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



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





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





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





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




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




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





max

2Hν

2Hν

Why is Throughput Manipulation Not Selected?



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.



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

igcc power plant design of dispatch...

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