design of massive energy storage systems for igcc based...

Design of Massive Energy Storage Systems for IGCC Based Electric Power Generation

Donald J. Chmielewski

Illinois Institute of Technology

*

BOP with

more profit

BOP with

less profit

OSSOP

EDOR’s due to

different controller

tunings

*

NETL Seminar – April 2011

Department of Chemical and Biological Engineering


Outline

• Motivation

• Profit Based Controller Design

• Dispatchable IGCC

• Market Responsive Control

• Examples



Power Management

Power Produced Equals Power Consumed



Power Management with Renewable Power

Power Produced Power Consumed



Power Management With Renewable Power

Renewable Dispatchable Load

MW

M

W



Energy Storage Route



Diversification Route Gas Turbine

Coal Fired

Renewable

Grid

Demand Nuclear

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Diversification Route Gas Turbine

Coal Fired

Renewable

Grid

Demand

Nuclear

IGCC

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Motivation Structure

Merchant Perspective Utility Perspective

Driven by Consumers Reliability Requirements Focused on Capital Costs

Driven by Opportunity Attention to Market Prices Focused on Revenue




Electricity Spot Price

Merchant Perspective


19 20 21 22-10

0

10

20

30

40

Time (days)

Cen

ts p

er

kW

hr

RTP Electricity

Forecasted Data




Outline

• Motivation




• Examples



Performance in Time Series

time

T(t)

F(t)

F(sp)

T(sp)

time

F(max)

T(max)

F

F

CA, T



Performance in Phase Plane

)(tF

)(tT

*



Dynamic Operating Region

)(tF

)(tT

*



Steady-State Operating Line

)(tF

)(tT

*



Optimal Operating Point

)(tF

)(tT

*

Decrease F

Increase T

Increase

conversion

Increase

production



Optimal Operating Point: Another Possibility

)(tF

)(tT

* Increase F

Increased

production rate



Optimal Operating Point: Another Possibility

)(tF

)(tT

*

Increase F

Increased

production rate



Requires Different Tuning of the Controller

)(tF

)(tT

*


Illinois Institute of Technology Peng et al. (2005)

*

*

BOP with

more profit

BOP with

less profit

Max

Profit

EDOR’s due to

different controller

tunings

Profit Control (Simultaneous BOP and Controller Selection)



Profit Control Example (FCC)

Regenerator and Separator (dynamic):

Riser (pseudo steady state):

(adapted from Loeblein & Perkins, 1999)




Process Constraints:

Profit Function:

Fgs Fgl and Fugo are product flows

(gasoline, light gas and unconverted oil).




0 5 10 15 20

x 10-4

25

26

27

28

29

30

31

32

Inle

t A

ir (

kg

/s)

Oxygen Mass Fraction5 5.5 6 6.5 7 7.5 8 8.5

x 10-3

280

300

320

340

360

380

400

Cata

lyst

Flo

w (

kg

/s)

Fraction of Coke in Regenerator

0.0125 0.013 0.0135 0.014 0.0145 0.015

990

992

994

996

998

1000

Reg

en

era

tor

Tem

p (

K)

Coke Fraction in Separator

Fixed Controller Free Controller

785 790 795 800 805 810 815

990

992

994

996

998

1000

Cyclo

ne T

em

pera

ture

(K

)

Separator Temperature (K)

Profit Control vs. Fixed Controller Back-off



FCC Profit

Gross Profit Diff from OSSOP ($/day) ($/day) OSSOP $36,905 $0.0 Fixed Control $34,631 - $2,274 Profit Control $35,416 - $1,489 Improves profit by 2%



Outline

• Motivation

• Economic Based Controller Design



• Examples



Integrated Gasification Combined Cycle



IGCC with Synthesis Gas Storage

Synthesis

Gas

Storage



Synthesis Gas Storage

Electric

Power

Coal,

Oxygen

and

Steam

Gasification

and

Gas

Cleaning

Units

Energy

Conversion

Units

(Gas

Turbines

and Electric

Generators)

Gas Storage Unit



Value of Electric Power Generated

0 5 10 15 200

2

4

6

8

10

Time (days)

Co

nve

rte

d G

as V

alu

e (

ce

nts

/ m

3)



Where is the Disturbance?

Electric

Power

Coal,

Oxygen

and

Steam

Gasification

and

Gas

Cleaning

Units

Energy

Conversion

Units

(Gas

Turbines

and Electric

Generators)

Gas Storage Unit

0 5 10 15 200

2

4

6

8

10

Time (days)

Co

nve

rte

d G

as V

alu

e (

ce

nts

/ m

3)




Process Constraints:

Profit Function:

Fgs Fgl and Fugo are product flows

(gasoline, light gas and unconverted oil).




Outline

• Motivation




• Examples



Electric Price Model

White

Noise

Input

Shaping

Filter

Sequence with

Electricity Price

Characteristics




Prediction

of

Electricity

Price

White

Noise

Input

Shaping

Filter

State

Estimator

and/or

Predictor

Sequence with

Electricity Price

Characteristics

Measured

Electricity

Price




Prediction

of

Electricity

Price

White

Noise

Input

Shaping

Filter

State

Estimator

and/or

Predictor

Sequence with

Electricity Price

Characteristics

Measured

Electricity

Price

19 20 21 22-10

0

10

20

30

40

Time (days)

Cen

ts p

er

kW

hr

RTP Electricity

Forecasted Data



Model Predictive Control

maxmax

0)(

)(0 and )(0

:include sConstraint

storagein amount ~ )( and

production of velocity the~ )(

(or value) price predicted the~ )( where

)(*)(max

StSvtv

tS

tv

tp

dttvtp

pp

p

e

T

petvp



Model Predictive Control

maxmax

0)(

)(0 and )(0

:include sConstraint

storagein amount ~ )( and

production of velocity the~ )(

(or value) price predicted the~ )( where

]*[)(*)(max

StSvtv

tS

tv

tp

RvpEdttvtp

pp

p

e

pe

T

petvp



System Design

RvpEdttvtp pe

T

petvp

*)(*)(max

0)(

) )(0 and )(0 ( maxmaxStSvtv pp

?impact and does How maxmaxRSvp



System Design

RvpEdttvtp pe

T

petvp

*)(*)(max

0)(

) )(0 and )(0 ( maxmaxStSvtv pp

question sanswer thicannot route MPC

?impact and does How maxmaxRSvp



Rescaling of Price

Shaping

Filter

Process

Model

p'e(t)

Manipulated

Variables

(Controller is

u=Lx)

vp(t)

E[p'e*vp]

a w(t)

)'( ee pp a



Correlation of Price and Production

)()(then tptv ep a

0)'( If 2 pe vpE ee pp a' and



Average Revenue Calculated Analytically

)()(then tptv ep a

0)'( If 2 pe vpE ee pp a' and

0][][2][ Also,222 ppee vEvpEpE aa

][][][ 222

ppee vEvpEpE aa

RvpEpE pee ][][ 2

a



Controller Design for Maximum Revenue

aa

RL

cR,

max

0)'( 2 pe vpE

2max2)( pp vvE

2max2 )(SSE

])[(2

eR pEc



Resulting Controller is Linear and Problem is Convex

aa

RL

cR,

max

0)'( 2 pe vpE

2max2)( pp vvE

2max2 )(SSE

])[(2

eR pEc

Lxu



Levelized Revenue

max2,

max1,

,,, maxmaxmax Scvcc LpLR

SvL pp

aa

0)'( 2 pe vpE

2max2)( pp vvE

2max2 )(SSE



Levelized Revenue

max2,

max1,

,,, maxmaxmax Scvcc LpLR

SvL pp

aa

0)'( 2 pe vpE

2max2)( pp vvE

2max2 )(SSE

Non-Convex Problem

(but branch and bound yields global solutions)



Outline

• Motivation




• Examples



IGCC with Synthesis Gas Storage

Synthesis

Gas

Storage



Synthesis Gas Storage Example (Small Storage Unit)

Electric

Power

Coal,

Oxygen

and

Steam

Gasification

and

Gas

Cleaning

Units

Energy

Conversion

Units

(Gas

Turbines

and Electric

Generators)

Gas Storage Unit

0 5 10 15 200

2

4

6

8

10

Time (days)

Co

nve

rte

d G

as V

alu

e (

ce

nts

/ m

3)

0 5 10 15 200

2

4

6

8

10

Time (days)

Ga

s V

olu

me

in

Sto

rag

e (

mill

ion

m3)

0 5 10 15 200

5

10

15

20

25

30

35

40

45

Time (days)

Vo

lum

etr

ic F

low

(m

illio

n m

3 / d

ay)



Synthesis Gas Storage Example (Larger Storage Unit)

Electric

Power

Coal,

Oxygen

and

Steam

Gasification

and

Gas

Cleaning

Units

Energy

Conversion

Units

(Gas

Turbines

and Electric

Generators)

Gas Storage Unit

0 5 10 15 200

2

4

6

8

10

Time (days)

Co

nve

rte

d G

as V

alu

e (

ce

nts

/ m

3)

0 5 10 15 200

2

4

6

8

10

Time (days)

Ga

s V

olu

me

in

Sto

rag

e (

mill

ion

m3)

0 5 10 15 200

5

10

15

20

25

30

35

40

45

Time (days)

Vo

lum

etr

ic F

low

(m

illio

n m

3 / d

ay)



Synthesis Gas Storage Example (Changes in Revenue)

0 2 4 6 8 10 12 14 16 18 200

1

2

3

4

Time (days)

Re

ve

nu

e (

mill

ion

do

llars

/ d

ay)

Average Revenue

- No Storage: $1.00 million per day (plot not depicted)

- Small Storage: $1.04 million per day.

- Large Storage: $1.15 million per day.



IGCC with Compressed Air Storage

Compressed

Air

Storage



Cryogenic Air Separation Unit (CASU)

Compressor

Air

Pre

trea

tmen

t

Expander

Crude Liquid Oxygen

GOX

N2 Rich Vapor

Air

Compressed

Air

Liquid N2

GOX GN2

Low Pressure Column

High Pressure Column

Hea

t E

xch

anger

Expander

Work



Why Not O2 Storage?

Compressor

Air

Pre

trea

tmen

t

Expander

Crude Liquid Oxygen

GOX

N2 Rich Vapor

Air

Compressed

Air

Liquid N2

GOX GN2

Low Pressure Column


Hea

t E

xch

anger

Expander

Work

Cryogenic Distillation

Unit has very large

Response Time

Typically the slowest

unit of the whole

IGCC process



Why Compressed Air Storage?

Compressor

Air

Pre

trea

tmen

t

Expander

Crude Liquid Oxygen

GOX

N2 Rich Vapor

Air

Compressed

Air

Liquid N2

GOX GN2

Low Pressure Column


Hea

t E

xch

anger

Expander

Work

Compressed Air

Storage

Compressed

Air

95% of CASU power is

used by the Main Air

Compressor.

Main Air Compressor

can respond quickly.

Distillation Unit can

still be run at constant

throughput.



Dispatchability

η0

Compressed Air

Storage

Ps, Ts, Vs

F0, Ps

C0

F0, P0

F2, Ps

η4

F4, Pe

C4

F6, P0

F5, Pe

to

CASUCG

from

IGCC

CN

to grid



Capital Cost of Storage

Compressor

Water Reservoir

Compressed Air Inventory

CASUc

$0.09/m3 of total

volume

or

$0.54/m3 of working

volume.



Capital Cost of Storage

Compressor

Water Reservoir

Compressed Air Inventory

CASUc

$0.09/m3 of total

volume

or

$0.54/m3 of working

volume.

Compressor cost is $1600/kW



Dispatchable Operation?

26 26.5 27 27.5 28 28.5 29 29.5 30

0

0.2

0.4

Mm

3

Volume of Storage

26 26.5 27 27.5 28 28.5 29 29.5 30-100

0

100

200

$/M

Wh

time, day

Price of Electricity

c

d

26 26.5 27 27.5 28 28.5 29 29.5 30

0

20

40

60

80

MW

CASU Main Compressor

26 26.5 27 27.5 28 28.5 29 29.5 30

0

20

40

60

80

MW

Stroage Main Compressor

a

b



Changes in Revenue

26 26.5 27 27.5 28 28.5 29 29.5 30-1

-0.5

0

0.5

1

1.5

2

2.5

Revenue,

mill

ion $

/day

time, day

with storageno storage

b



Levelized Annual Revenue

Compressor

Costs

Storage

Costs

Levelized

Revenue

Without

Storage $96M - $368M/yr

With

Storage $192M $0.2M $377M/yr



Sensitivity to Spot Price Characteristics

26 26.5 27 27.5 28 28.5 29 29.5 30-100

-50

0

50

100

150

200P

rice o

f E

lectr

icity,

$/M

Wh

time, day

low-pricehigh-price

d



Sensitivity to Spot Price Characteristics

26 26.5 27 27.5 28 28.5 29 29.5 30

61.861.9

CA

SU

Main

Com

pre

ssor,

MW

time, day

26 26.5 27 27.5 28 28.5 29 29.5 300

10

20

30

40

50

60

70

high-price market

low-price marketa

26 26.5 27 27.5 28 28.5 29 29.5 30

0.001

0.01

Str

oage M

ain

Com

pre

ssor,

MW

time, day26 26.5 27 27.5 28 28.5 29 29.5 30

0

10

20

30

40

50

60

low-price market

high-price marketb

26 26.5 27 27.5 28 28.5 29 29.5 30

1e-4

3e-4

Volu

me o

f S

tora

ge,

Mm

3

time, day

26 26.5 27 27.5 28 28.5 29 29.5 300

0.1

0.2

0.3

0.4

0.5

c high-price market

low-price market

26 26.5 27 27.5 28 28.5 29 29.5 30-100

-50

0

50

100

150

200P

rice o

f E

lectr

icity,

$/M

Wh

time, day

low-pricehigh-price

d



Storage at Wrong Pressure?

η0

Compressed Air

Storage

Ps, Ts, Vs

F0, Ps

C0

F0, P0

F2, Ps

η4

F4, Pe

C4

F6, P0

F5, Pe

to

CASUCG

from

IGCC

CN

to grid



Pressure Reduction?

η0

Compressed Air

Storage

Ps, Ts, Vs

F0, Ps

C0

F0, P0

ηe ηc

F2, Ps

F3, P0

F2, Pe F3, Pe

F1, Pe

E2

C2 C3

η4

F4, Pe

C4

F6, P0

F1, Pe

F5, Pe

to

CASUCG

from

IGCC

CN

to grid



Influence of Storage Pressure

20 40 60 80 100

368

370

372

374

376

378

380

382

levelie

zd a

nnual re

venue,

mill

ion $

/yr

Ps, atm

electricity price=60, SwE

=60 $/MWh

electricity price=60, SwE

=35 $/MWh

none storage

η0

Compressed Air

Storage

Ps, Ts, Vs

F0, Ps

C0

F0, P0

ηe ηc

F2, Ps

F3, P0

F2, Pe F3, Pe

F1, Pe

E2

C2 C3

η4

F4, Pe

C4

F6, P0

F1, Pe

F5, Pe

to

CASUCG

from

IGCC

CN

to grid



Conclusions

1. Numerous opportunities to make IGCC more dispatchable.

2. Direct response to price changes can be implemented with Model Predictive Control.

3. Alternatively, a linear controller can be designed for market responsiveness.

4. Non-convex, but global methods can be used to size equipment.

5. Results very sensitive to Spot Price Characteristics and Storage Pressures.




Merchant Perspective Utility Perspective






Electric Power System Design (with Professor Hug, ECE CMU)

Gas Turbine

PC Boiler

Renewable Transmission

Grid

Consumer Demand

Energy Storage



System Disturbances

0 5 10 15 20 25 30 35 400

100

200

300

400

500

600

700

Pr (

MW

)

Days

Consumer Demand

1.5 2 2.5 3 3.5 4 4.5 5

10

12

14

16

Pow

er

Load (

GW

)

Forcasted Data

Simulated Data

Renewable Power

Generated




Unified Perspective






HVAC Control (with Professor Muehleison, CAEE, IIT)

Volume of Air

(the Room) Air

Processing

Unit

Contaminant

Source: Sc

Solid

Material

Frcy , Troom , Croom

Frcy , Tcool , Croom

Ffresh , Tcool , Cfresh

Ffresh , Troom , Croom

Ffresh , Toutside , Cfresh

Ffresh , Troom , Croom

Troom , Croom

Tsolid

Energy Usage

Heat

Leakage

(Toutside

measured)

(Cfresh = 0)

Control Variables: Troom and Croom

Manipulated Variables: Frcy and Ffresh

Disturbances: Toutside and Sc

(Tcool = 20oC)

Demand-Controlled Ventilation (DCV)

for Indoor Air Quality (IAQ)



Thermal Energy Storage (TES)

In HVAC systems TES is used for

Load Leveling and to shift usage to Off-Peak Hours

Volume

of Air

(the

Room)

Cooling

Unit

Heat to

CoolerHeat from

Room

Troom

Energy

Usage

Heat

Leakage

Toutside

TES

Unit

Heat to

TES Unit



Thermal Energy Storage (Revenue Comparisons)

59 60 61 62

0

10

20

30

Time (days)

Ele

ctr

icity C

osts

($ /

day)

59 60 61 62

0

0.05

0.1

0.15

$ /

kW

hr

Electricity Price

One Ton TES Unit

Five Tons TES Unit

Ten Tons TES Unit

Average Cooling Costs:

One ton: $8 per day

Five tons: $7 per day (14% savings)

Ten tons: $6 per day (25% savings)



Acknowledgements

• Students and Collaborators:

Amit Manthanwar

Dr. Jui-Kun Peng (ANL)

Benjamin P. Omell and Ming-Wei Yang

Professor Javad Abbasian (ChBE, IIT)

• Funding:

National Science Foundation (CBET – 0967906)

Graduate and Armour Colleges, IIT

Chemical & Biological Engineering Department, IIT

design of massive energy storage systems for igcc based...

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